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Keywords:

  • statistical quality control;
  • multivariate control chart;
  • t-Student distribution;
  • trimmed estimators

Control charts are the most popular tool for monitoring production quality. In traditional control charts, it is usually supposed that the observations follow a multivariate normal distribution. Nevertheless, there are many practical applications where the normality assumption is not fulfilled. Furthermore, the performance of these charts in the presence of measurement errors (outliers) in the historical data has been improved using robust control charts when the observations follow a normal distribution. In this paper, we develop a new control chart for t-Student data based on the trimmed T2 control chart (inline image) through the adaptation of the elements of this chart to the case of this distribution. Simulation studies show that a inline image control chart performs better than T2 in t-Student samples for individual observations. Copyright © 2012 John Wiley & Sons, Ltd.