Abstract
This paper describes a proposed framework for multivariate process control chart recognition. The proposed methodology uses the Artificial Neural Networks (ANNs) to recognize set of subclasses of multivariate abnormal patterns, identify the responsible variable(s) on the occurrence of abnormal pattern and classify the abnormal pattern parameters. The performance of the proposed approach has been evaluated using a real case study. The numerical and graphical results are presented which demonstrate that the approach performs effectively in control chart multivariate pattern recognition. In addition, accurately identifies and classifies the parameters of the errant variable(s).