An algorithm developed based on a multi-layer neural network with learning is proposed for discriminating deterministic functions in the presence of random distortions and under the conditions of both parametric and non-parametric a priori uncertainty. Statistical simulation methods help to establish its operability and sufficiently high efficiency. The conditions for the expediency of its application in practical applications are stated.