In this paper, we present a system that recognizes objects in a jumble, verifies them, and then determines some essential configurational information, such as which ones are on top. The approach is to use three-dimensional models of the objects to find them in range data. The matching strategy starts with a distinctive edge feature, such as the edge at the end of a cylindrical part, and then "grows" a match by add ing compatible features one at a time. (The order of features to be considered is predetermined by an interactive, off-line, feature-selection process.) Once a sufficient number of com patible features has been detected to allow a hypothesis to be formed, the verification procedure evaluates it by comparing the measured range data with data predicted according to the hypothesis. When all the objects in the scene have been hy pothesized and verified in this manner, a configuration- understanding procedure determines which objects are on top of others by analyzing the patterns of range data predicted from all the hypotheses. We also present experimental results of the system's performance in recognizing and locating castings in a bin.