The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.