An effective approach for bone image analysis, including segmentation and recognition is presented in this paper. Bone segmentation is carried out by K-means clustering of the bone image, whereas the recognition phase is based on feature extraction and two-level statistical classification. The proposed approach has applications in medicine and veterinary anatomy studies, orthopedics, paleontology and archaeology. Several image features, including geometric and moment invariants (regular and Zernike), are derived for recognition. The first-level classification is used to distinguish different kinds of bone and the second-level to recognize the right animal to which the bone belongs. Two-dimensional structures, called cluster-property and cluster-features matrices, have been employed to evaluate different bone characteristics. Experimental results for the first-level recognition exhibit better performance of the geometric features compared to moment invariants and Zernike moments. On the other hand, Zernike moments showed supremacy in differential diagnosis at the second level to recognize animals.