A variety of paint and fingernail polish samples, which were visually similar, but had different chemical compositions and formulations, was analyzed using quadrupole static secondary ion mass spectrometry (SIMS). Coating distinction was easily achieved in many cases because of the presence of dominant ions derived from the components of the coating, which could be observed in the SIMS spectra. In other instances, coating distinction was difficult within a product line because of spectral complexity; for this reason and because of the large numbers of spectra generated in this study, multivariate statistical techniques were employed, which allowed the meaningful classification and comparison of spectra. Partial Least Squares (PLS) and Principal Component Analysis (PCA) were applied to quadrupole SIMS data. PCA showed distinct spectral differences between most spectral groups, and also emphasized the reproducibility of the SIMS spectra. When using PLS analysis, reasonably accurate coating identification was achieved with the data. Overall, the PLS model is more than 90% effective in identifying the spectrum of a particular coating, and nearly 100% effective at telling which coating components represented in the PLS models are not present in a spectrum. The level of spectral variation caused by sample bombardment in the SIMS analysis was investigated using Fourier transform infrared spectroscopy (FT-IR) and quadrupole static SIMS. Changes in the FT-IR spectra were observed and were most likely a result of a number of factors involving the static SIMS analysis. However, the bulk of the sample is unaltered and may be used for further testing.