Data processing forms a crucial step in metabolomics studies, impacting upon data output quality, analysis potential and subsequent biological interpretation. This chapter provides an overview of data processing and analysis of GC-MS- and LC-MS-based metabolomics data. Data preprocessing steps are described, including the different software available for dealing with such complex datasets. Multivariate techniques for the subsequent analysis of metabolomics data, including principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are described with illustrations. Steps for the identification of potential biomarkers and the use of metabolite databases are also outlined.