A computer vision system is proposed, in which the recogni-tion of an object involves two interacting processes: model retrieval and model verification. The goal of the model retrieval process is to generate a proper structural description of the object in the input image, and use the description to retrieve candidate object models from the associative memory of the vision system. The present study explores one way of deriving such an object shape description from a single image. Regularity constraints and a preference rule are used to restrict the solutions to a preferred interpretation of geometric contours. Local interpretation is then propagated to neighboring regions. Through a proper control on the interaction between constraints and consistency checking, a rough object description in terms of visible surface orientations can be gener-ated. A computer vision system using this approach has been imple-mented and it is described in some details.