Abstract The adaptive multivariate EWMA (AMEWMA) and adaptive multivariate CUSUM (AMCUSUM) charts are recently proposed as they provide an overall good detection over a range of mean shift sizes than their non-adaptive conventional counterparts. In this paper, we propose an adaptive MEWMA (AMEWMA) chart for monitoring the infrequent changes in the mean of a multivariate normally distributed process. The idea is to consider an unbiased estimator of the mean shift using an MEWMA statistic, and then use it to adaptively update the smoothing constant of the MEWMA chart. By using Monte Carlo simulations, the run length characteristics of the AMEWMA chart are computed. The AMEWMA chart is comprehensively compared with the existing AMEWMA and AMCUSUM charts in terms of the average run length when detecting mean shifts in different ranges. It is found that the proposed chart is able to perform uniformly and substantially better than the existing charts. Both real and simulated datasets are considered to support the theory.