In many quality control applications, the necessary distributional assumptions to correctly apply the traditional parametric control charts are either not met or there is simply not enough information or evidence to verify the assumptions. It is well known that performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not require a specific distributional assumption to be valid, so-called nonparametric or distribution-free charts, are desirable in practice. In this paper, a simple to use multivariate nonparametric control chart is introduced. The chart is based on the multivariate two sample Mann-Withney Wilcoxon test for equality of location vectors of two populations. Using simulated data we show that there are situations in which the Mann-Withney multivariate control chart has a better performance compared with T2 control chart.

Assume that we have m independent random samples each of size n from Np(; ) and our goal is to test whether or
not the ith sample is an outlier (i=1,2,…..m). To date it is well known that a test statistics exist whose null distribution
is Betta and given the relationship between Betta and F distribution, an F test statistic can be used. In the statistical
literature however a clear and precise proof is not accessible and in some cases the proof is incomplete. In this paper
a precise and relatively clear proof is given and through simulation, capability and weakness of the test is considered.