Time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. A frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. There have been identified standard patterns which are commonly present when using the X chart; its detection depends on human judgement supported by norms and graphical criteria. In the last few years, it has been demonstrated that Artificial Neural Networks (ANN’s) are useful to predict the type of time-series pattern instead of the use of rules. However, the ANN control parameters have to be fixed to values that maximize its performance. This research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the FuzzyARTMAP ANN such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition
