A new approach to fusion of target identity and state vector information acquired from multiple sensors, based on combining approximate reasoning concepts derived from fuzzy logic and evidential reasoning fields, is presented. The primary emphasis is on the fusion of target identity information, assumed to be available in the form of fuzzy class membership estimates, to derive more robust target identification. The fusion of track state vector information is viewed as an essential ingredient of the identity fusion process and is carried out with only minimal sophistication to minimize computational demands that enable real-time implementation. The methodology, although designed for fusion of fuzzy identity information, can be adapted to cases wherein the information provided is in the form of crisp labels from unreliable or imperfect sensors. This is accomplished with a learning phase wherein the imperfectness of the sensor inputs are modeled using the available ground truth data to derive fuzzy membership estimates for the various sensoridentity reports. The methodology takes on a sensor-to-track fusion (rather than sensor-to-sensor fusion) approach to enable a variable number of sensors and minimize dependence on congruency of sensor reports along the time line. The fuzzy identity membership estimates of the tracks along with track state vectors are updated through appropriate data association (based on near-enough-a-neighbor principles) of the incoming sensor reports. The estimates also include a measure of ignorance that monotonically decreases as more and more sensor reports are fused with the tracks. Details of the algorithmic approach are presented followed by results of application of the methodology to some real-world data. These results clearly show the efficacy of the process, which synergistically combines fuzzy logic and evidential reasoning concepts.