System control includes sensor management, user refinement, and mission accomplishment (SUM). An example of simultaneous tracking and identification includes (1) mission goals of resource appropriation and goal priorities, (2) user selection of targets and areas of coverage, and (3) fusion of data and sensory information. Many sensor management (SM) approaches are data-driven which includes filtering, aggregation, and normalization; however that does not include intelligent design. A top-down approach would facilitate the use of the right sensor, collecting the needed information, at the correct time. In order to better design SM algorithms, we utilize sensor, target, environmental, and automatic target recognition performance models for automatic target exploitation (ATE) prediction. Similar to pruning nodes in a Bayes net aggregation, a sensor manager can utilize the operating conditions (OCs) {i.e. sensor, target, environment} to condition the cost function, sensor-to-target assignment constraints, and scheduling times. An example is presented of determining task value of electro-optical sensor selection and scheduling based on the range to target, target size, and environmental conditions (e.g. occlusions). The key aspect of the SMOC provides accurate assignment and scheduling based on up-to-date database information, a capabilities matrix, and pragmatic sensor use to improve task satisfaction.