We discuss the design of a neural system that learns to separate nominal from faulty instances of an analog circuit in a low dimensional measurement space. The key novelty of the proposed system is that it successively establishes a separation hypersurface of order that adapts to the intrinsic complexity of the problem. Thus, it performs excellent classification even in the presence of complex distributions. The test criterion for classifying a circuit is simply the location of its measurement pattern with respect to the separation hypersurface. Despite its simplicity, this criterion is, by construction, strongly correlated to the performance parameters of the circuit and does not rely on fault models.