1. A computer implemented method for determining a change in state of a target being monitored comprising the steps of: measuring feature data associated with said target during a defined time duration; processing using a computer processor, real time sensor data from said target using a neural network data structure that has defined therein convex hulls having regions of ambiguity corresponding to each of a set of target classes corresponding to target features, wherein the intersection of convex hulls for each pair of classes defines the region of ambiguity in feature space, wherein the neural network data structure uses training data to receive the real time sensor data and recognize signal characteristics which indicate the presence of information relating to a target class designation; transitioning, according to the neural network data structure, to another feature space having different features, in response to the neural network data structure's processing of measurement data of feature vectors within an ambiguous region of feature space defined by convex hulls; and determining using a computer processor, a next sensor measurement data for obtaining additional measured feature data of the target according to a current state of the graph data structure and the region of ambiguity; and repeating said processing, transitioning and determining steps until the change in state of the target is classified.