This paper describes a system which uses fuzzy relational inference logic (an implementation of Dempster-Schafer evidential reasoning) to combine identity estimates derived from a network of distributed sensing nodes. The temporal association is mediated through the use of a multi-target tracking system built around a decentralized Kalman filter, and different combination rules are applied for the cases of consistent or conflicting evidence. Comparisons are drawn with approaches based on the explicit computation of identity probability estimates and their combination. The availability of good estimates of target identity can be used to resolve some of the basic data association ambiguities in the multi-target tracking system. This paper reviews some background material in data fusion. Then describes the vision component of the decentralized data fusion test-bed which has been used as the basis for the system considered here. The basic identity fusion algorithm is then presented, and a comparison drawn with an alternative, Bayesian approach. The possible extension of the system to include neural network based target classification is also considered.