While Machine Learning (ML) Automatic Target Recognition (ATR) represents the state-of-the art in target recognition, model-based ATR plays a valuable role. Model-based ATR complements machine learning ATR approaches by filling a near-term niche. While explainable Artificial Intelligence (AI) is not yet fully realized, model-based ATR serves to validate machine learning recognition decisions, and thus instills confidence in ML target calls. Alternatively, model-based ATR can act as a stand-alone ATR component, particularly in scenarios in which a small number of targets are of interest, e.g., “target-of-the-day” engagements. Model-based ATR approaches need no training data, and thus provide an alternative to machine learning approaches in the absence of sufficient quantities of real, or sufficiently high-fidelity synthetic, training data. In this paper, we present an approach to model-based ATR, called Shape-Based ATR (SB-ATR), which captures salient target shape information for recognizing targets in wide-area satellite imagery. SB-ATR finds the right blend of coarse 3-D target shape abstraction and target realism to provide robustness against target variations and environmental operating conditions, while simultaneously providing high-performance target recognition. The approach uses newer, robust forms of image correlation for matching a predicted target shape against the image. Shape prediction searches over target pose, and uses satellite metadata and solar geometry to generate realistic target shape and shadow predictions. The correlation matchers provide tolerance to illumination variations, moderate occlusions, image distortions and noise, and geometric differences between models and real targets. We present technical details of the shape-based approach, and provide numerical target recognition results on real-world satellite imagery demonstrating performance.