Photointerpretation is a computationally expensive process. An opportunistic knowledge-based approach to the problem has been developed and proven effective for target recognition on the ground and in the air. The system hypothesizes about the identity of regions in an image based on their geometric and intensity properties. Each region looks for support for its interpretation based on its spatial relationships with other hypotheses. In this paper parallel implementation using a supporting spatial representation on an eight node Hypercube (a coarse-grained, local memory, message passing machine) is described. The algorithm would be equally suitable for a local area network of workstations. A quadtree data structure representing each image region can be compactly and efficiently coded into a few integers; hence, passing regions between computer nodes becomes a reasonable algorithmic device.