Clustering and classification of replicated biological data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper we introduce a simple approach based on a `spike-and-slab' mixture model that is fast, automatic, allows classification, clustering and variable selection in a single framework, and can also handle replicated data. Simulation shows that our approach compares well with other recently proposed methods. The ideas are illustrated by application to microarray and metabolomic data.