Shadows can add significant edge and surface detail to imagery and thus substantially increase the performance of automated correlation guidance systems. A shadow-generation algorithm was implemented to increase the accuracy of synthetic imagery used to simulate visible, near-infrared, and far-infrared sensors. Initially, a data base was established in which all surfaces were represented by a list of vertices and material codes and arranged according to a scheme of a priori masking priority. Each surface was then clipped against updated clipping polygons representing the silhouette of all previous surfaces that had higher masking priorities as viewed from the position of the light source. The resulting hidden surface was inserted into the data base and flagged as a shadow for gray-scale prediction by the appropriate sensor model. Because each surface is compared to a union of polygons rather than to individual surfaces, this algorithm is computationally efficient for use with large data bases.