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Abstract-This paper reports on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal understanding research is surveyed, and the selected implementation is described with its performance in terms of correct classification rates and robustness to noise. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control is given using functional link nets and an explanation technique is designed to help neural signal understanding. The results of this are compared to those of a knowledge-based signal interpretation system within the context of the same specific instrument and data.

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