Abstract. This work demonstrates the potential of multivariate image analysis methods in the extraction of useful, problem dependent information from SIMS images. Specific algorithms have been developed to classify SIMS micrographs manually as well as automatically. A feature selection has been achieved by means of principal component analysis with a subsequent image classification.As an application example for these improved digital image processing tools chemical phases within a soldered industrial metal sample have been identified. This is of highly practical value as it was assumed that during the soldering process inhomogeneities occur along the joint site which cause a cracking of the brazed material under mechanical strain conditions. Key words: secondary ion mass spectrometry (SIMS), imaging, classification, principal component analysis (PCA).Digital image analysis, initially developed in the sixties for exploration purposes, has spread as a new powerful tool over a whole variety of disciplines. Physical dimensions do not set any limits to its application possibilities: ranging from satellite based sensing in space, down to the atomic level of scanning tunneling microscopy [1], digital image processing techniques are now extensively employed. They have also proved to be very attractive for chemical analytical aims where they provide the advantage of visualization of data otherwise difficult to grasp, for example because of very large data sets, and also the advantage of possible quantification of purely optical, qualitative information. The digitized images used for such operations are statistical to the core because a numerical value can be allocated to each to their pixels and therefore they obviously contain more information than mere size or shape. Digital image processing techniques applied to such micrographs directly induce a calculation of the underlying data. Vice versa, statistical operations within