In this paper the application and performance of Artificial Neural Networks (ANN) to the problem of sensor data fusion is reported for an experimental system, Tracker. The task of sensor data fusion involves integrating numerous data streams, originating from disparate sensors, into a consistent model that represents the pertinent higher level features of the environment as well as presenting an assessment of their significance. In the case of the modern naval environment, the problem central to many tactical data fusion systems is the need for rapid acquisition and interpretation of the information. In a potentially hostile situation the time taken to perform such an assessment is severely limited and a rapid and accurate response is vital. This paper describes the application of ANN to tactical sensor data fusion and the automated processing of the radar behaviors for various vehicle types. In particular the tasks of target and behavioral identification for both automated surveillance and support tasks are highlighted as important in the modern naval environment. The experimental research program divided the analysis of the radar tracks into three distinct categories. These were (1) target identification, (2) behavioral analysis (target task identification) and (3) threat assessment. A Knowledge Based System (KBS), previously developed by the Defense Research Agency, was used as a comparison. In addition, support functions in the conventional KBS, such as clutter identification, were also evaluated using ANN based technology. The results of this research program are reported in this paper.