With recent advances in sensory and mobile computing technology, many interesting applications involving moving objects have emerged. One of them is identification of suspicious movements : an important problem in homeland security. The objects in question can be vehicles, airplanes, or ships; however, due to the sheer volume of data and the complexities within, manual inspection of the moving objects would require too much manpower. Thus, an automated or semi-automated solution to this problem would be very helpful. That said, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule- and Motif-based Anomaly Detection in M oving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rule-based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Such rules are easily readable and can be easily provided to humans to aid better inspection of moving objects.