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A neural classifier that learns to separate the nominal from the faulty instances of a circuit in a measurement space is developed. Experimental evidence, which demonstrates that the required separation boundaries are, in general, nonlinear, is presented. Unlike previous solutions that build hyperplanes, the proposed classifier is capable of drawing nonlinear hypersurfaces. A new circuit instance is classified through a simple test, which examines the location of its measurement pattern with respect to these hypersurfaces. The classifier is trained through an algorithm that probably converges to the optimal separation boundary. Additionally, a feature selection algorithm interacts with the classifier to identify a discriminative low-dimensional measurement vector. Despite employing only a few measurements, the test criteria established by the neural classifier are strongly correlated to the performance parameters of the circuit and do not rely on a presumed fault model.