The current generation of correlation systems attempting to provide a Single Integrated Picture (SIP) have concentrated on improving quality from the situational awareness (SA) and tracking perspective with limited success, while having not addressed the combat identification (CID) issue at all. Furthermore, decision time has lengthened, not decreased, as more and more sensor data are made available to the commanders; much of which is video in origin. Many efforts are underway to build a network of sensors including the Army's Future Combat System (FCS), Air Force Multi-mission Command and Control Aircraft (MC2A), Network-Centric Collaborative Targeting (NCCT), and the follow-on to the Navy's Cooperative Engagement Capability (CEC). Each of these programs has the potential to increase precision of the targeting data with successful correlation algorithms while eliminating dual track reports, but none have combined or will combine disparate sensor data into a cohesive target with a high confidence of identification. In this paper, we address an architecture that solves the track correlation problem using frequency plane pattern recognition techniques that also can provide CID capability. Also, we discuss statistical considerations and performance issues.