The vector-matrix multiply and winner-take-all structure is presented as a general-purpose, low-power, compact, programmable classifier architecture that is capable of greater computation than a one-layer neural network, and equivalent to a two-layer perceptron. The classifier generates event outputs and is suitable for integration with event-driven systems. The main sources of mismatch, temperature dependence, and methods for compensation are discussed. We present measured data from simple linear and nonlinear classifier structures on a 0.35-μm chip and analyze the power and computing efficiency for scaled structures.