Currently, detection, tracking, and classification functions are performed sequentially, for example, tracks are initiated after objects have been detected. This leads to utilization of only partial information for each of the surveillance functions: the information about the object being on a specific track is not utilized for the object detection. This, in turn, leads to unnecessary limitations on system performance or to stringent and expensive sensor requirements. We have developed a novel approach to enhancing surveillance functions by combining several functions and by utilizing all the available information for each function, based on the maximum likelihood adaptive neural network (MLANS). The MLANS capability for a general model-based processing permits combining such functions as data correlation, detection, track estimation, and classification. In this application, a generic MLANS architecture implements a model that combines classification model based on statistical distributions of object features with the dynamical model of object motion. The MLANS learning mechanism results in a maximum likelihood estimation of the model parameters, yielding concurrent estimates of data association probabilities, track parameters, and object classification. This novel approach to tracking results in a dramatic improvement of performance: the MLANS tracking exceeds performance of existing tracking algorithms due to optimal utilization of all the available data.