This paper deals with the problem of identifying and testing a number of extreme sample elements (t = 1, 2, 3 and 4) as significant outliers in a sample of size n from a K-dimensional normal distribution with unknown parameters. Accommodation of detected outliers is effected through outlier-robust estimation of multivariate location (mean vector) and dispersion (variance-covariance matrix).
