diff --git a/onnxruntime/core/providers/cpu/nn/batch_norm.h b/onnxruntime/core/providers/cpu/nn/batch_norm.h
index be9bc3368e..1febfac86d 100644
--- a/onnxruntime/core/providers/cpu/nn/batch_norm.h
+++ b/onnxruntime/core/providers/cpu/nn/batch_norm.h
@@ -75,9 +75,11 @@ class BatchNorm : public OpKernel {
     const TensorShape& x_shape = X->Shape();
     Tensor* Y = p_op_kernel_context->Output(0, x_shape);
 
+    // X shape is [N, C, D1, D2, ... Dn], but it can also be 1-D according to onnx spec:
+    // "The op also accepts single dimension input of size N in which case C is assumed to be 1"
     const auto& dims_vec = x_shape.GetDims();
     const size_t N = onnxruntime::narrow<size_t>(dims_vec[0]);
-    const size_t C = onnxruntime::narrow<size_t>(dims_vec[1]);  // assume NCHW as per the spec
+    const size_t C = dims_vec.size() == 1 ? 1 : onnxruntime::narrow<size_t>(dims_vec[1]);
 
     // calculate sample_size (per individual channel)
     size_t sample_size = 1;
diff --git a/onnxruntime/core/providers/cpu/nn/batch_norm_helper.h b/onnxruntime/core/providers/cpu/nn/batch_norm_helper.h
index ccecbabfa3..b5aa522f71 100644
--- a/onnxruntime/core/providers/cpu/nn/batch_norm_helper.h
+++ b/onnxruntime/core/providers/cpu/nn/batch_norm_helper.h
@@ -28,6 +28,9 @@ class BatchNormHelper {
     // NHWC dependent shape: X
     // All other shapes are assumed to be in NCHW layout?
     const auto& x_dims = X->Shape().GetDims();
+    if (x_dims.size() < 1) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "Invalid input X: NumDimensions() < 1");
+    }
 
     // If x_dims size < 2, num_channels defaults to 1.
     int64_t num_channels;