The main purpose of this paper is twofold: (1) to present a neural-network based methodology for monitoring process shift in the presence of autocorrelation; and (2) to demonstrate the power, the effectiveness, and the adaptability of this approach. The proposed neural network uses the effective and efficient extended delta-bar-delta learning rule and can be trained with the powerful back-propagation algorithm. The comparative study on AR(1) processes shows that the performance of this neural-network based monitoring scheme is superior to that of SCC, X, EWMA, EWMAST and ARMAST control charts in most instances. Moreover, the network output can also provide information about the shift magnitude. The study of run length distributions suggests that further improvement on designing such neural networks is possible. The adaptability of the neural-network approach is demonstrated through the flexible design of the training data set. To further improve run length properties under various shift magnitudes, alternative control heuristics are proposed.
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