Mixtures of factor analyzers enable model-based clustering to be undertaken for high-dimensional microarray data, where the number of observations n is small relative to the number of genes p. Moreover, when the number of clusters is not small, for example, where there are several different types of cancer, there may be the need to reduce further the number of parameters in the specification of the component-covariance matrices. A further reduction can be achieved by using mixtures of factor analyzers with common component-factor loadings (MCFA), which is a more parsimonious model. However, this approach is sensitive to both non-normality and outliers, which are commonly observed in microarray experiments. This sensitivity of the MCFA approach is due to its being based on a mixture model in which the multivariate normal family of distributions is assumed for the component-error and factor distributions.