Many large, complex target tracking scenarios, such as full-scale strategic missile attacks or low-observable tactical engagements, require both advanced algorithms and state-of-the-art parallel processing to produce accurate, timely results. In this paper, approaches for implementing multiple object, multiple hypothesis tracking algorithms on a massively parallel computer are presented and evaluated. Multiple hypothesis tracking algorithms offer improved performance over more traditional approaches, albeit at the expense of increased processing and storage requirements. Massively parallel array processors can deliver this needed computational power, assuming the algorithms can be efficiently mapped onto this restrictive class of architectures. Algorithms are described here for all the functions within the multiple hypothesis approach. These algorithms are then benchmarked using the distributed array of processors (DAP) series from Active Memory Technology, Inc. Results of these benchmarks show that the multiple hypothesis tracking algorithms can be successfully implemented on array processors, displaying processing times that increase sublinearly with the number of objects under surveillance.