diff --git a/sanity_check.py b/sanity_check.py
index 81e8ee5..50af5ce 100644
--- a/sanity_check.py
+++ b/sanity_check.py
@@ -31,15 +31,23 @@ def sanity_check(vtable_diff, minor_patch_diff):
     diff1 = load_diff_file(vtable_diff)
     diff2 = load_diff_file(minor_patch_diff)
 
-    for (old_opcode, new_opcodes) in diff2.items():
+    all_good = True
+    for old_opcode, new_opcodes in diff2.items():
         if old_opcode not in diff1:
             if len(new_opcodes) < 50:
                 print(f"Missing old opcode in vtable diff: {old_opcode}")
+                all_good = False
             continue
 
         other_new_opcodes = diff1[old_opcode]
         if list(other_new_opcodes)[0] not in new_opcodes:
             print(f"vtable diff mismatch for case {old_opcode} => {other_new_opcodes}")
+            all_good = False
+
+    if all_good:
+        print("Sanity check OK")
+    else:
+        print("Errors detected")
 
 
 if __name__ == "__main__":
diff --git a/vtable_alignment.py b/vtable_alignment.py
new file mode 100644
index 0000000..a190f02
--- /dev/null
+++ b/vtable_alignment.py
@@ -0,0 +1,157 @@
+import click
+import json
+
+from vtable_diff import extract_opcode_data
+from utils import eprint
+
+
+def needleman_wunsch(old_seq, new_seq, similarity, gap_penalty):
+    """
+    The Needleman-Wunsch algorithm adapted from
+    https://github.com/farhanma/pyseq/blob/master/functions1_3.py
+
+    Returns (alignment, alignment_score)
+    """
+
+    # Stage 1: Create a zero matrix and fills it via algorithm
+    n, m = len(old_seq), len(new_seq)
+    mat = []
+    for i in range(n + 1):
+        mat.append([0] * (m + 1))
+    for j in range(m + 1):
+        mat[0][j] = gap_penalty * j
+    for i in range(n + 1):
+        mat[i][0] = gap_penalty * i
+    for i in range(1, n + 1):
+        for j in range(1, m + 1):
+            mat[i][j] = max(
+                mat[i - 1][j - 1] + similarity.lookup(old_seq[i - 1], new_seq[j - 1]),
+                mat[i][j - 1] + gap_penalty,
+                mat[i - 1][j] + gap_penalty,
+            )
+
+    # Stage 2: Computes the final alignment, by backtracking through matrix
+    alignment = []
+    i, j = n, m
+    while i and j:
+        score, scoreDiag, scoreUp, scoreLeft = (
+            mat[i][j],
+            mat[i - 1][j - 1],
+            mat[i - 1][j],
+            mat[i][j - 1],
+        )
+        if score == scoreDiag + similarity.lookup(old_seq[i - 1], new_seq[j - 1]):
+            alignment.append((old_seq[i - 1], new_seq[j - 1]))
+            i -= 1
+            j -= 1
+        elif score == scoreUp + gap_penalty:
+            alignment.append((old_seq[i - 1], None))
+            i -= 1
+        elif score == scoreLeft + gap_penalty:
+            alignment.append((None, new_seq[j - 1]))
+            j -= 1
+    while i:
+        alignment.append((old_seq[i - 1], None))
+        i -= 1
+    while j:
+        alignment.append((None, new_seq[j - 1]))
+        j -= 1
+
+    # Since we were backtracking, we reverse the collected alignment
+    alignment.reverse()
+
+    return alignment, mat[n][m]
+
+
+class Similarity:
+    def __init__(self, similarity_json_file):
+        with open(similarity_json_file) as f:
+            data = json.load(f)
+
+        self.old_opcodes = {
+            opcode: idx for (idx, opcode) in enumerate(data["old_opcodes"])
+        }
+        self.new_opcodes = {
+            opcode: idx for (idx, opcode) in enumerate(data["new_opcodes"])
+        }
+        self.__matrix = data["matrix"]
+
+    def lookup(self, old_opcode, new_opcode):
+        if old_opcode not in self.old_opcodes:
+            eprint(
+                f"WARNING: Could not find old opcode {hex(old_opcode)} in similarity matrix"
+            )
+            return 0
+        if new_opcode not in self.new_opcodes:
+            eprint(
+                f"WARNING: Could not find new opcode {hex(new_opcode)} in similarity matrix"
+            )
+            return 0
+        i = self.old_opcodes[old_opcode]
+        j = self.new_opcodes[new_opcode]
+        return self.__matrix[i][j]
+
+
+@click.command()
+@click.argument(
+    "old_exe", type=click.Path(exists=True, dir_okay=False, resolve_path=True)
+)
+@click.argument(
+    "new_exe", type=click.Path(exists=True, dir_okay=False, resolve_path=True)
+)
+@click.argument(
+    "similarity_json_file",
+    type=click.Path(exists=True, dir_okay=False, resolve_path=True),
+)
+def vtable_alignment(old_exe, new_exe, similarity_json_file):
+    """
+    A more generalized version of vtable_diff. Generates an opcode
+    diff file by running a sequence alignment algorithm and attempting
+    to find the optimal global alignment of the vtable opcodes
+    from different exe versions.
+
+    Requires a similarity matrix generated from generate_similarity_matrix.py.
+
+    This script outputs to stdout, so pipe it to a json file.
+
+    The format of the output is a list (all fields are optional):
+
+    \b
+    [
+        {
+            "old": [opcode],
+            "new": [opcode],
+        },
+        ...
+    ]
+
+    Example:
+
+    python vtable_alignment.py ffxiv_dx11.old.exe ffxiv_dx11.new.exe similarity.json > diff.json
+    """
+    old_opcodes_db = extract_opcode_data(old_exe)
+    new_opcodes_db = extract_opcode_data(new_exe)
+
+    old_seq = [opcode for opcode in old_opcodes_db.values()]
+    new_seq = [opcode for opcode in new_opcodes_db.values()]
+
+    similarity = Similarity(similarity_json_file)
+    alignment, score = needleman_wunsch(old_seq, new_seq, similarity, -1)
+    eprint(f"Alignment score: {score}")
+
+    diff = []
+    for old, new in alignment:
+        if old is None:
+            eprint("New opcode did not find matching old one:", hex(new))
+            diff.append({"old": [], "new": [hex(new)]})
+        elif new is None:
+            eprint("Old opcode did not find matching new one:", hex(old))
+            diff.append({"old": [hex(old)], "new": []})
+        else:
+            diff.append({"old": [hex(old)], "new": [hex(new)]})
+
+    print(json.dumps(diff, indent=2))
+
+
+if __name__ == "__main__":
+    vtable_alignment()