The volume of data that must be processed to characterize the performance of target detection algorithms over a complex parameter space requires automated analysis. This paper discusses a methodology for automatically scoring the results from a diversity of detectors producing several different forms of detected regions. The ability to automatically score detector outputs without using full target templates or models has advantages. Using target descriptors-primarily target sizes and locations-reduces the computational cost of matching detected regions against truthed targets in various scenes. It also diminishes the size of and the difficulty of creating an image-truth database. Theoretical considerations are presented. Overcoming issues associated with using limited truth information is explained. Concepts and use of the Auto-Score package are also discussed. The performances of several different laser radar (LADAR) target detectors, applied to imagery containing scenes with targets and both natural and man-made clutter, have been characterized with the aid of Auto-Score. Automatic scoring examples are taken from this domain. However, the scoring process is applicable to detectors operating on other problems and other kinds of data as well. The target-descriptor scoring concept and Auto-Score implementation were originated to support the development of a configurable automatic target recognition (ATR) system for LADAR data, under the auspices of the Office of Naval Research.