The application of multivariate analytical tools enables simplification of TOF-SIMS datasets so that useful information can be extracted from complex spectra and images, especially those that do not give readily interpretable results. There is however a challenge in understanding the outputs from such analyses. The problem is complicated when analysing images, given the additional dimensions in the dataset. Here we demonstrate how the application of simple pre-processing routines can enable the interpretation of TOF-SIMS spectra and images. For the spectral data, TOF-SIMS spectra used to discriminate bacterial isolates associated with urinary tract infection were studied. Using different criteria for picking peaks before carrying out PC-DFA enabled identification of the discriminatory information with greater certainty. For the image data, an air-dried salt stressed bacterial sample, discussed in another paper by us in this issue, was studied. Exploration of the image datasets with and without normalisation prior to multivariate analysis by PCA or MAF resulted in different regions of the image being highlighted by the techniques. (C) 2008 Elsevier B. V. All rights reserved.