A package which provides a multivariate approach to evaluate the quality of an array that examines the ‘Mahalanobis distance’ of its quality attributes from those of other arrays. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Its advantage is that it has a clear statistical foundation, it uses the correlation structure of the various QC measures, it is easy to apply, and it is computationally lightweight. These properties make MDQC a useful diagnostic technique suitable for large datasets. MDQC performs a robust multivariate analysis of the quality measures provided in the QC report while taking into account their correlation structure.