We present a Bayesian sparse representation (BSR) model for synthetic aperture radar (SAR) image classification. It involves the design of the sub-categorization scheme for the samples, the formulation of the BSR model, and the implementation of the classification decision. First, the training samples of each target category are sub-categorized into multiple subclasses. The discriminative features are then extracted from each sample within one subclass. These same features from all samples are gathered to construct a sub-dictionary. After collecting all sub-dictionaries from all features, the sparse reconstructions are performed for all features. A BSR framework is employed for such purposes. Finally, a fusion strategy is applied to the residuals to predict the class label of the test sample. By sub-categorizing the samples into multi-clusters, the proposed model decreases the intra-class variations between the samples and thus improve the representation ability of the features to different targets. The test results using real field data demonstrate that the proposed method has superiority to some state-of-the-art methods.