Copula theory is one of the fast-growing branches of statistics dealing with make up the multivariate models from marginal ones by considering several types of dependence. This paper develops Hotelling’s T2 and exponentially weighted moving average (EWMA) control charts when observations come from a mixture copula model including, Gumbel, Clayton, and Frank copulas with normal marginals. This mixture model enables the quality inspector to cover several dependence levels of observations from weak and moderate to strong in positive values by Kendalls tau. To assess the performance of the proposed chart, extensive MonteCarlo simulations were used based on the average run length (ARL) metric for both in-control and out-of-control situations. A real data analysis is also presented for more illustration.

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