diff --git a/circuitforge_core/input/gestures/normalizer.py b/circuitforge_core/input/gestures/normalizer.py
new file mode 100644
index 0000000..259b4ec
--- /dev/null
+++ b/circuitforge_core/input/gestures/normalizer.py
@@ -0,0 +1,32 @@
+"""
+Landmark normalization for MediaPipe hand landmarks.
+
+Converts raw (21, 3) landmark array into a 63-element translation- and
+scale-invariant feature vector suitable for gesture classifiers.
+"""
+import numpy as np
+
+
+def normalize_hand(points: np.ndarray) -> np.ndarray:
+    """
+    Normalize 21 MediaPipe hand landmarks into a scale/translation-invariant
+    63-element feature vector.
+
+    Steps:
+        1. Translate so wrist (landmark 0) is at origin.
+        2. Scale so distance from wrist to middle-finger MCP (landmark 9) = 1.0.
+           If that distance is near-zero (degenerate hand), return zeros.
+        3. Flatten to shape (63,).
+
+    Args:
+        points: (21, 3) float32 array — raw MediaPipe landmark coords.
+
+    Returns:
+        (63,) float32 feature vector.
+    """
+    pts = points.astype(np.float32).copy()
+    pts -= pts[0]                          # translate: wrist → origin
+    scale = float(np.linalg.norm(pts[9])) # wrist-to-middle-MCP distance
+    if scale > 1e-6:
+        pts /= scale
+    return pts.flatten()
diff --git a/tests/test_input/test_gestures/test_normalizer.py b/tests/test_input/test_gestures/test_normalizer.py
new file mode 100644
index 0000000..cff2d6d
--- /dev/null
+++ b/tests/test_input/test_gestures/test_normalizer.py
@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+from circuitforge_core.input.gestures.normalizer import normalize_hand
+
+
+def _synthetic_hand(scale: float = 1.0, offset: float = 0.0) -> np.ndarray:
+    """21 landmarks, wrist at offset, middle MCP at offset + (scale, 0, 0)."""
+    pts = np.zeros((21, 3), dtype=np.float32)
+    # All landmarks start at the offset (roughly at the wrist)
+    for i in range(21):
+        pts[i] = [offset, 0.0, 0.0]
+    # Then define a few key landmarks relative to wrist
+    pts[0] = [offset, 0.0, 0.0]   # wrist
+    pts[9] = [offset + scale, 0.0, 0.0]  # middle MCP at distance scale from wrist
+    pts[1] = [offset + 0.1 * scale, 0.05 * scale, 0.0]  # thumb
+    pts[5] = [offset + 0.4 * scale, 0.2 * scale, 0.0]  # index
+    return pts
+
+
+def test_output_shape():
+    pts = _synthetic_hand()
+    result = normalize_hand(pts)
+    assert result.shape == (63,)
+
+
+def test_translation_invariance():
+    pts_a = _synthetic_hand(offset=0.0)
+    pts_b = _synthetic_hand(offset=5.0)
+    np.testing.assert_allclose(normalize_hand(pts_a), normalize_hand(pts_b), atol=1e-5)
+
+
+def test_scale_invariance():
+    pts_small = _synthetic_hand(scale=0.5)
+    pts_large = _synthetic_hand(scale=2.0)
+    np.testing.assert_allclose(normalize_hand(pts_small), normalize_hand(pts_large), atol=1e-5)
+
+
+def test_zero_scale_does_not_crash():
+    """All landmarks at same point — degenerate hand. Should return zeros, not raise."""
+    pts = np.zeros((21, 3), dtype=np.float32)
+    result = normalize_hand(pts)
+    assert result.shape == (63,)
+    assert not np.any(np.isnan(result))
+
+
+def test_dtype_is_float32():
+    pts = _synthetic_hand()
+    result = normalize_hand(pts)
+    assert result.dtype == np.float32