We describe an experimental model-based automatic target recognition (ATR) system, called XTRS, for recognizing 3-D vehicles in real or synthetic, ground-based or airborne, 2-D laser-radar range and intensity images. The key to recognition is a new generic matching engine that compares image events (e.g., silhouettes) and their constituent primitives to some appearance-model (AM) hierarchy, which describes how 3-D, possibly articulated, objects appear in the imagery. We describe each of the system's components, i.e., the event characterization (for extracting events from images and decomposing the events into primitives), the models, the matching, and the control. XTRS may also perform low-level information fusion by constructing feature-indicating interest images and combining them into an overall interest image used for locating an event. Examples of processing and performance results are given for real CO2 ground-based laser-radar imagery. We also report on a performance evaluation of XTRS based on synthetic data where important parameters (e.g., range to target) are systematically varied. Overall, more than 1500 range and intensity image pairs have been used throughout the development of XTRS.