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Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key to the performance of object recognition. In this paper, we propose a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The knowledge about the problem domain is incorporated in primitive features that are used in the synthesis of composite features by CGP using domain-independent primitive operators. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can discover good composite features to distinguish objects from clutter and to distinguish among objects belonging to several classes. The comparison with other classical classification algorithms is favorable to the CGP-based approach proposed in this paper.