Many existing automatic target recognition (ATR) schemes do not fully exploit the information contained in the local spatial structure of targets. However, discussions with image analysts make it clear that the shape of a target alone provides a great deal of information about the nature of the target. A human analyst will identify component shapes that make up an observed target such as a long, thin barrel, corner structures and straight parallel and perpendicular edges. Spatial relationships between these morphological components can be used to recognize targets. This approach is robust to different target configurations and articulations because the analyst has built up in his / her mind a model of how the morphological components of a target interact. In contrast, established model-based ATR approaches can characterize the target for only one particular choice of configuration and articulation and the model must be exercised repeatedly to investigate the parameter space which can be a time consuming process. For this reason, the feasibility of identifying morphological features and using models for their spatial relationships to perform ATR has been investigated. Methods for extracting spatial information from SAR images of targets despite the associated speckle noise have been investigated. A means for incorporating this spatial information into a classification scheme has then been developed. It has been shown that significant ATR performance can be achieved on SAR images of real targets based only on localized spatial structure.