Modeling human behavior and activity patterns for recognition or detection of anomalous events has attracted significant research interest in recent years, particularly among the video surveillance community. An anomalous event might be characterized as an event that deviates from the normal or usual, but not necessarily in an undesirable manner, e.g., An anomalous event might just be different from normal but not a suspicious event from the surveillance stand point. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability. Therefore, most works model the expected patterns on the scene, instead, based on video sequences where anomalous events do not occur. Assuming images captured from a single camera, we propose a novel spatiotemporal feature descriptor, called Histograms of Optical Flow Orientation and Magnitude (HOFM), based on optical flow information to describe the normal patterns on the scene, so that we can employ a simple nearest neighbor search to identify whether a given unknown pattern should be classified as an anomalous event. Our descriptor captures spatiotemporal information from cuboids (regions with spatial and temporal support) and encodes both magnitude and orientation of the optical flow separately into histograms, differently from previous works, which are based only on the orientation. The experimental evaluation demonstrates that our approach is able to detect anomalous events with success, achieving better results than the descriptor based only on optical flow orientation and outperforming several state-of-the-art methods on one scenario (Peds2) of the well-known UCSD anomaly data set, and achieving comparable results in the other scenario (Peds1).