In recent years, numerous prototypical systems have been developed for multisensor data fusion. A typical data fusion process operates on sensor parametric data (e.g., data related to target position or attribute data) in order to develop an order of battle, provide an evaluation of tactical situations, or assess tactical threats. This model, developed by the Data Fusion Sub- panel (DFS) of the Joint Directors of Laboratories, partitions fusion processing into four conceptual levels. Ancillary functions in a fusion system include the human computer interface, data base management, source-preprocessing functions, and communications. Military applications for data fusion span a broad range including fusion of data on board a single platform for identifying other platforms (e.g., identification--friend or foe--neutral systems), threat warning systems, situation assessment, and threat assessment systems. Large scale systems such as the All-Source Analysis System (ASAS) or the Joint Surveillance, Targeting, and Reconnaissance System (JSTARS) provide for direction, coordination, and fusion of both ground-based and airborne sensors to aid in the effective management of a ground based battlefield environment. Such systems have become ever more sophisticated. Indeed, many of the prototypical systems utilize advanced identification techniques such as knowledge-based or expert systems. Dempster-Shafer interface techniques, adaptive neural networks, and sophisticated tracking algorithms. While much research is being performed to develop and apply new algorithms and techniques, little work has been performed to determine how well such methods work or to compare alternative methods against a common problem. The issues of system performance and system effectiveness are keys to establishing how well an algorithm, technique, or collection of techniques perform, and then the extent to which these techniques may be used to achieve success on an operational mission.