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Clustering techniques have been used to perform image segmentation, to detect lines and curves in the images and to solve several other problems in pattern recognition and image analysis. In this paper we apply clustering methods to a new problem domain and present a new method based on a cluster-structure paradigm for the recognition of 2-D partially occluded objects. The cluster-structure paradigm entails the application of clustering concepts in a hierarchical manner. The amount of computational effort decreases as the recognition algorithm progresses. As compared to some of the earlier methods, which identify an object based on only one sequence of matched segments, the new technique allows the identification of all parts of the model which match with the apparent object. Also the method is able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object. The method has been evaluated with respect to a large number of examples where several objects partially occlude one another. A summary of the results is presented.