Recent work has suggested that target shadows in synthetic aperture radar (SAR) images can be used effectively to aid in target classification. The method outlined in this paper has four steps - segmentation, representation, modeling, and selection. Segmentation is the process by which a smooth, background-free representation of the target's shadow is extracted from an image chip. A chain code technique is then used to represent the shadow boundary. Hidden Markov modeling is applied to sets of chain codes for multiple targets to create a suitable bank of target representations. Finally, an ensemble framework is proposed for classification. The proposed model selection process searches for an optimal ensemble of models based on various target model configurations. A five target subset of the MSTAR database is used for testing. Since the shadow is a back-projection of the target profile, some aspect angles will contain more discriminatory information then others. Therefore, performance is investigated as a function of aspect angle. Additionally, the case of multiple target looks is considered. The capability of the shadow-only classifier to enhance more traditional classification techniques is examined.