This paper presents a robust and efficient skeleton-based graph matching method for object recognition and recovery applications. The novel feature is to unify both object recognition and recovery components into an image understanding system architecture, in which a complementary feedback structure can be incorporated to alleviate processing difficulties of each component alone. The idea is firstly to recognize the preliminary extracted object from a set of models using the new skeleton graph matching method, then to apply the a priori shape information of the identified model for accurate object recovery. The output of the system is the recognized and segmented object. The skeleton graph matching method is illustrated by recognizing a set of tool and animal silhouette examples with the presence of geometric transformations (translation, rotation, scaling, reflection), shape deformations and noise. Experiments of object recovery using MR knee images, have shown satisfactory results.