The inference about the statistical properties of quality control methodologies is based on the assumptions of normality and independence. In real industrial environments though process data is often correlated or exhibits some serial dependence affecting the efficiency of Statistical Process Control (SPC) methodologies. New technology gives managers the option of using more sophisticated SPC models which more accurately reflect the process being monitored, by relaxing some of the assumptions. The aim of this paper is to present, to apply and to evaluate control charts that are designed to account for autocorrelation.
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