An artificial neural network, combining signals from a large number of plasma diagnostics, was used to estimate the high- beta disruption boundary in the DIII-D tokamak. It is shown that inclusion of many diagnostic measurements results in a much more accurate prediction of the disruption boundary than that provided by the traditional Troyon limit. A trained neural network constitutes a non-linear, non-parametric model of the disruption boundary. Through the analysis of the input-output sensitivities, the relative statistical significance of various diagnostic measurements (plasma parameters) for the determination of the disruption boundary is directly assessed and the number of diagnostics used by the neural network model is reduced to the necessary minimum. The neural network is trained to map the disruption boundary throughout most of the discharge. As a result, it can predict the high- beta disruption boundary on a time-scale of the order of 100 ms (much longer than the precursor growth time), which makes this approach ideally suitable for real time application in a disruption avoidance scheme. Owing to the relative simplicity of the required computations, the neural network is easily implemented in a real time system. A prototype of the neural network disruption alarm was installed within the DIII-D digital plasma control system, and its real time operation, with a typical time resolution of 10 ms, was demonstrated