Recently, mathematical morphology has been used to develop efficient and statistically robust 2-dimensional edge detectors. These edge detectors have been shown to outperform most mask and differentiation based edge detectors. In this paper, we introduce a general robust N-dimensional morphological edge detector that outperforms any of the previously developed morphological edge detectors. We compare the statistical performance of our edge detector with that of the previously developed 2-D morphological edge detector on images with various noise levels. Finally, we will also include some examples of our edge detector's output on both 2 and 3-dimensional images to compare with other operators.