We present a reconfigurable optical implementation of an acousto-optic algebra processor, based on a calomel (single crystal mercurous chloride) matrix-vector multiplier. Two successful applications are demonstrated: neural network training and curve detection. In the first, a perceptron learns two-input Boolean functions. In the second, a connectionist model of the Hough transform is generalized to handle arbitrary curves. Experiments for line detection and circle detection are performed, and the Hough transform's throughput is solely limited by the update latency of the acousto-optic unit. (C) 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99)01007-7].