diff --git a/src/dialect/onnx/onnx_ops.cpp b/src/dialect/onnx/onnx_ops.cpp
index 4b68fe7..2380f5a 100644
--- a/src/dialect/onnx/onnx_ops.cpp
+++ b/src/dialect/onnx/onnx_ops.cpp
@@ -318,12 +318,28 @@ void ONNXMatMulOp::inferShapes() {
   if (!getOperand(0).getType().isa<RankedTensorType>() ||
       !getOperand(1).getType().isa<RankedTensorType>())
     return;
-  auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
-  auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
+
+  auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
+  auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
+
   SmallVector<int64_t, 2> dims;
-  dims.emplace_back(lhsTy.getShape()[0]);
-  dims.emplace_back(rhsTy.getShape()[1]);
-  getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
+  auto lhsShape = lhsTy.getShape();
+  auto rhsShape = rhsTy.getShape();
+  if (lhsShape.size() == 1 && rhsShape.size() == 1) {
+    // Special case when both arrays are 1-dimensional and according to
+    // numpy rules the types need to be extended to 1xN and Nx1. Helper sizes
+    // need to be removed after the multiplication but cannot be removed if all
+    // remaining sizes are 1.
+    if (lhsShape[0] != -1 && rhsShape[0] != -1 &&
+        lhsShape[0] != rhsShape[0])
+      emitError("Attempt to multiply incompatible matrices.");
+    dims.emplace_back(1);
+  } else {
+    dims.emplace_back(lhsShape.size() == 1 ? 1 : lhsShape[0]);
+    dims.emplace_back(rhsShape.size() == 1 ? 1 : rhsShape[1]);
+  }
+
+  getResult()->setType(RankedTensorType::get(dims, lhsTy.getElementType()));
 }
 
 // TODO: