Multivariate analysis (PC-CVA and GA-CVA) was carried out on time-of-flight secondary ion mass spectra (ToF-SIMS) derived from 16 bacterial isolates associated with urinary tract infections, with an objective of extracting the spectral information relevant to their species-level discrimination. The use of spectral pre-processing, such as removal of the dominant peaks prior to analysis and analysis of the dominant peaks alone, enabled the identification of 37 peaks contributing to the principal components-canonical variates analysis (PC-CVA) discrimination of the bacterial isolates in the mass range of m/z 1-1000. These included signals at m/z 70, 84, 120, 134, 140, 150, 175 and 200. A univariate statistical analysis (Kruskal-Wallis) of the signal intensities at the identified m/z enabled an understanding of the discriminatory basis, which can be used in the development of robust parsimonious models for predictive purposes. The utility of genetic algorithm (GA)-based feature selection in identifying the discriminatory variables is also demonstrated. A database search of the identified signals enabled the biochemical origins of some these signals to be postulated.