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This project developed an automated controller based on an artificial neural network and evaluated its applicability in a real‐time environment. This capability was developed within the context of a small angle negative ion source on the Discharge Test Stand at Los Alamos. The controller processes information obtained from the beam current wave form, developing a figure of merit (fom) to determine the ion source operating conditions. The fom is composed of the magnitude of the beam current, the stability of operation, and the quietness of the beam. Using no knowledge of operating conditions, the controller begins by making of rough scan of the four‐dimensional operating surface. This surface uses as independent variables the anode and cathode temperatures, the hydrogen flow rate, and the arc voltage. The dependent variable is the fom described above. Once the rough approximation of the surface has been determined, the network formulates a model from which it determines the best operating point. The controller takes the ion source to that operating point for a reality check. As real data is fed in, the model of the operating surface is updated until the neural network’s model agrees with reality. The controller then uses a gradient ascent method to optimize the operation of the ion source. Initial tests of the controller indicate that it is remarkably capable. It has optimized the operation of the ion source on six different occasions bringing the beam to excellent quality and stability.