The problem of track-to-track association has been considered until recently in the literature only for pairwise associations. In view of the extensive recent interest in multisensor data fusion, the need to associate simultaneously multiple tracks has arisen. This is due primarily to bandwidth constraints in real systems, where it is not feasible to transmit detailed measurement information to a fusion center but, in many cases, only local tracks. As it has been known in the literature, tracks of the same target obtained from independent sensors are still dependent due to the common process noise. This paper derives the likelihood function for the track-to-track association problem from multiple sources, which forms the basis for the cost function used in a multidimensional assignment algorithm that can solve such a large scale problem where many sensors track many targets. While a recent work derived the likelihood function under the assumption that the track errors are independent, the present paper incorporates the (unavoidable) dependence of these errors.