Surface-based techniques for protein comparison and classification typically require a compact surface representation, capable of effectively condensing its description. In this paper we propose an original template-matching algorithm for multi-feature surface clustering in the biochemical context. The effectiveness of our clustering algorithm in capturing surface similarities is then discussed within a larger framework for protein classification based on surface comparison, with the support of tests performed on a dataset including 25 proteins.