The complexity of the mass spectra obtained by static time-of-flight-secondary ion mass spectrometry (ToF-SIMS) demands high-throughput data analysis techniques that rapidly process and interpret the resulting data. We have used ToF-SIMS to analyze adsorbed protein films. Positive ion mass spectra from different protein films are challenging to differentiate due to the absence of unique, identifying peaks between the different spectra. Therefore, the multivariate pattern recognition techniques principal component analysis (PCA) and linear discriminant analysis (LDA) have been employed to differentiate the spectra of different proteins and understand the major sources of variation in these spectra. Because of its supervised nature, LDA enhanced discrimination between groups and classification of unknowns when compared with PCA. However, PCA was able to provide better information on the sources of variation in the data set. Both PCA and LDA are important in the analysis of static ToF-SIMS spectra from organic samples. (C) 2002 Elsevier Science B.V. All rights reserved.