A target-oriented method for sensor data fusion is being developed to provide practical, automated, multi-sensor tracking in multiple-target environments of any size. To provide computational tractability for such a system indicates the exploitation of inherent parallelism to the maximum. This method employs an object-oriented system architecture to partition the task in the way that exploits the inherent parallelism to the maximum. Partitioning by target track offers the greatest scope for processing concurrency, and forms the basis of the design. The approach involves the allocation of independent, asynchronous logical processes to track individual targets on a one-to-one basis. The tracking processes each contain identical track initiation, data correlation and tracking algorithms, and are entirely independent of each other. Incoming sensor data is assessed, by all the tracking processes independently and concurrently, for reference to their target tracks, and, if so, is then used to update the track. Dedicated data routing processes are used to optimise the data throughput. An important feature of the target-oriented architecture is that system growth is easily achieved, by the straightforward replication of the individual tracking processes. Such growth demands only a linear increase in the number of such processes with the size of the en-vironment. This structure, being inherently modular, also lends itself to a dispersed, multi-platform implementation. The architecture has been extended to track clusters of targets as well as individuals, where all the cluster members may or may not be individually resolvable. Under contract to the US DoD SDIO, this concept has been refined to perform multi-target, multi-sensor track extraction under a variety of conditions, particularly when some or all of the sensors are devoid of an inherent tracking capability.