1. A method for processing an m-dimensional vector-valued measurement process to estimate an n-dimensional vector-valued signal process, said method comprising the steps of: (1) selecting a recurrent neural network paradigm; (2) selecting an estimation error criterion; (3) generating training data comprising realizations of said signal process and corresponding realizations of said measurement process; (4) constructing a training criterion; (5) synthesizing said training data into a primary filter, which is a recurrent neural network of said recurrent neural network paradigm; (6) implementing said primary filter, specified by said recurrent neural network's architecture, weights and initial dynamic state; and (7) receiving one measurement vector of said measurement process at a time at at least one input terminal of the implementation of said primary filter and producing an estimate of one signal vector of said signal process at a time at at least one output terminal of the implementation of said primary filter.