Most pattern recognition tasks, such as regression, classification and novelty detection, can be viewed in terms of probability density estimation. A powerful approach to probabilistic modelling is to represent the observed variables in terms of a number of hidden, or latent, variables. One well-known example of a hidden variable model is the mixture distribution in which the hidden variable is the discrete component label. In the case of continuous latent variables we obtain models such as factor analysis. In this paper we provide an overview of latent variable models, and we show how a particular form of linear latent variable model can be used to provide a probabilistic formulation of the well-known technique of principal components analysis (PCA). By extending this technique to mixtures, and hierarchical mixtures, of probabilistic PCA models we are led to a powerful interactive algorithm for data visualization. We also show how the probabilistic PCA approach can be generalized to non-linear latent variable models leading to the Generative Topographic Mapping algorithm (GTM). Finally, we show how GTM can itself be extended to model temporal data.