Abstract: Motivated by the analysis of batch cytometric data, we consider the problem of jointly modelling and clustering multiple heterogeneous data samples. Traditional mixture models cannot be applied directly to these data. Intuitive approaches such as pooling and post-hoc cluster matching fails to account for the variations between the samples. In this talk, we consider a hierarchical mixture model approach to handle inter-sample variations. The adoption of a skew mixture model with random effects terms for the location parameter allows for the simultaneous clustering and matching of clusters across the samples. In the case where data from multiple classes of objects are available, this approach can be further extended to perform classification of new samples into one of the predefined classes. Examples with real cytometry data will be given to illustrate this approach.