diff --git a/src/datajoint/expression.py b/src/datajoint/expression.py
index a9a7ddfe7..3f75edeec 100644
--- a/src/datajoint/expression.py
+++ b/src/datajoint/expression.py
@@ -860,7 +860,10 @@ def to_arrays(self, *attrs, include_key=False, order_by=None, limit=None, offset
                     arr = np.array(values)
                 except ValueError:
                     # Variable-size data (e.g., arrays of different shapes)
-                    arr = np.array(values, dtype=object)
+                    # Must assign individually to avoid numpy broadcasting issues
+                    arr = np.empty(len(values), dtype=object)
+                    for i, v in enumerate(values):
+                        arr[i] = v
                 result_arrays.append(arr)
 
             if include_key:
diff --git a/tests/integration/test_fetch.py b/tests/integration/test_fetch.py
index 8cde34deb..dd556ff70 100644
--- a/tests/integration/test_fetch.py
+++ b/tests/integration/test_fetch.py
@@ -397,3 +397,63 @@ def test_to_arrays_without_include_key(lang):
     names, langs = result
     assert isinstance(names, np.ndarray)
     assert isinstance(langs, np.ndarray)
+
+
+def test_to_arrays_inhomogeneous_shapes(schema_any):
+    """Test to_arrays handles arrays of different shapes correctly.
+
+    Regression test for https://github.com/datajoint/datajoint-python/issues/1380
+    """
+    table = schema.Longblob()
+    table.delete()
+
+    # Insert arrays with different shapes that numpy would try to broadcast
+    table.insert(
+        [
+            {"id": 0, "data": np.random.randn(100)},  # shape (100,)
+            {"id": 1, "data": np.random.randn(100, 1)},  # shape (100, 1)
+            {"id": 2, "data": np.random.randn(100, 2)},  # shape (100, 2)
+        ]
+    )
+
+    # This should not raise ValueError
+    data = table.to_arrays("data", order_by="id")
+
+    # Should return object array with 3 elements
+    assert data.dtype == object
+    assert len(data) == 3
+
+    # Each element should preserve its original shape
+    assert data[0].shape == (100,)
+    assert data[1].shape == (100, 1)
+    assert data[2].shape == (100, 2)
+
+
+def test_to_arrays_inhomogeneous_shapes_second_axis(schema_any):
+    """Test to_arrays handles arrays differing on second axis.
+
+    Regression test for https://github.com/datajoint/datajoint-python/issues/1380
+    """
+    table = schema.Longblob()
+    table.delete()
+
+    # Insert arrays with different shapes on second axis
+    table.insert(
+        [
+            {"id": 0, "data": np.random.randn(100)},  # shape (100,)
+            {"id": 1, "data": np.random.randn(1, 100)},  # shape (1, 100)
+            {"id": 2, "data": np.random.randn(2, 100)},  # shape (2, 100)
+        ]
+    )
+
+    # This should not raise ValueError
+    data = table.to_arrays("data", order_by="id")
+
+    # Should return object array with 3 elements
+    assert data.dtype == object
+    assert len(data) == 3
+
+    # Each element should preserve its original shape
+    assert data[0].shape == (100,)
+    assert data[1].shape == (1, 100)
+    assert data[2].shape == (2, 100)