An experimental model-based automatic target recognition (ATR) system, XTRS, for recognizing 3-D vehicles in real or synthetic, groundbased or airborne, 2-D laser-radar range and intensity images is discussed. 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. The authors describe each of the system's components; i.e., the even 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 groundbased laser-radar imagery. The authors also report on a performance evaluation of XTRS based on synthetic data where importance parameters (e.g., range to target) are systematically varied. Overall, more than 1500 range and intensity image pairs have been used throughout XTRS's development.