THE CLASSICAL MULTIVARIATE HOTELLING T 2 Control Chart, WHICH IS WIDELY USED IN PRACTICE, IS VERY SENSITIVE TO THE DEVIATION FROM MULTIVARIATE NORMAL DISTRIBUTION, ESPECIALLY AS A RESULT OF OUTLIERS. ALTERNATIVELY THE ROBUST VERSION OF THIS Control Chart WHICH ARE USUALLY BASED ON ROBUST ESTIMATORS OF LOCATION AND DISPERSION PARAMETERS, ARE USED TO REMEDY THIS PROBLEM. TWO OF THE MOST IMPORTANT OF THESE ROBUST ESTIMATORS ARE MINIMUM VOLUME ELLIPSOID (MVE) AND MINIMUM COVARIANCE DETERMINANT (MCD). ALTHOUGH THE ROBUST MULTIVARIATE Control ChartS, HAVE AN ACCEPTABLE PERFORMANCE IN LOW VARIABLES DIMENSION SPACE, THEIR CAPABILITY OF OUTLIERS' DETECTION DECREASE AS THE NUMBER OF VARIABLES INCREASES. DUE TO THIS REASON IN THIS PAPER, THE PRINCIPAL COMPONENT ANALYSIS (PCA) IS SUGGESTED TO EMPLOY AS A STATISTICAL TECHNIQUE FOR REDUCING THE DIMENSION OF DATASET.