Analytical instrumentation such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides a tremendous quantity of data since an entire mass spectrum is saved at each pixel in an ion image. The analyst often selects only a few species for detailed analysis; the majority of the data are not utilized. Researchers at Sandia National Laboratory (SNL) have developed a powerful multivariate statistical analysis (MVSA) toolkit named AXSIA (Automated eXpert Spectrum Image Analysis) that looks for trends in complete datasets (e.g., analyzes the entire mass spectrum at each pixel). A unique feature of the AXSIA toolkit is the generation of intuitive results (e.g., negative peaks are not allowed in the spectral response). The robust statistical process is able to unambiguously identify all of the spectral features uniquely associated with each distinct component throughout the dataset. General Electric and Sandia used AXSIA to analyze raw data files generated on an Ion TofIV ToF-SIMS instrument. Here, we will show that the MVSA toolkit identified metallic contaminants within a defect in a polymer sample. These metallic contaminants were not identifiable using standard data analysis protocol. (C) 2004 Elsevier B.V. All rights