Polymorphic Torus is a novel interconnection network for SIMD massively parallel computers, able to support effectively both local and global communication. Thanks to this characteristic, Polymorphic Torus is highly suitable for computer vision applications, since vision involves local communication at the low-level stage and global communication at the intermediate- and high-level stages. In this paper we evaluate the performance of Polymorphic Torus in the computer vision domain. We consider a set of basic vision tasks, namely, convolution, histogramming, connected component labeling, Hough transform, extreme point identification, diameter computation, and visibility, and show how they can take advantage of the Polymorphic Torus communication capabilities. For each basic vision task we propose a Polymorphic Torus parallel algorithm, give its computational complexity, and compare such a complexity with the complexity of the same task in mesh, tree, pyramid, and hypercube interconnection networks. In spite of the fact that Polymorphic Torus has the same wiring complexity as mesh, the comparison shows that in all of the vision tasks under examination it achieves complexity lower than or at most equal to hypercube, which is the most powerful among the interconnection networks considered. © 1989 Springer-Verlag New York Inc.