The Probabilistic Multi-Hypothesis Tracker (PMHT) of Streit and Luginbuhl uses the EM algorithm and a slight modification of the usual target-tracking assumptions to combine data-association and filtering. The performance of the PMHT to date has been comparable to that of existing tracking algorithms; however, part of its appeal is a consistent and extensible statistical foundation, and it is the extension to the tracking of maneuvering targets which we explore in this paper. The basis, as with many algorithms designed for maneuvering targets, is of an underlying and hidden 'model-switch' process controlled by a Markov probability structure. Performance of the modified PMHT is investigated both for maneuvering and non-maneuvering targets. The improved performance observed in the latter case is somewhat surprising.