Principal component analysis (PCA) is a statistical method used to find combinations of variables or factors that describe the most important trends in the data. PCA has been combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) data to extract new information and find relations between species contained in complex systems. Monolayers of dipalmitoylphosphatidylcholine alone and mixed with palmitoyloleoylphosphatidylglycerol prepared using the Langmuir-Blodgett technique are discussed. PCA software provides image scores and corresponding loadings for each significant principal component. Image plots of the scores show the spatial distribution and intensity of the species defined by the loading plots (mass spectral features). The intensity and resolution of the image scores can result in substantial improvement over that of the regular TOF-SIMS images especially when static conditions are used for small analysis areas. Also, some of the effects of topography and matrix in the images can be removed, allowing for a better presentation of chemical variations.