Numerical techniques have been found useful in extracting information from the large data sets produced by time-of-flight secondary ion mass spectrometry (TOF-SIMS). Simple univariate approaches to quantitation in static SIMS, including spectral normalization, smoothing, curve fitting, and generation of spectral overlays, will be described. The complexity of TOF-SIMS data makes multivariate approaches particularly useful, and multivariate statistical techniques such as principal components analysis (PCA) will be described in detail. PCA, in simplest terms, reduces a data set to its essential elements. A significant advantage of PCA over univariate methods is that it greatly compresses the data by combining variables. This is particularly beneficial in TOF-SIMS, where a data set may consist of a 256x256 pixel matrix in which each pixel contains a complete mass spectrum consisting of upwards of 100,000 mass channels. Applications of PCA which will be described here include automating complex tasks previously performed manually by the analyst, finding non-obvious information in data sets, and distinguishing relevant from non-relevant information. Situations in which PCA is particularly applicable are those in which differences are sought. Typical examples might be a comparison of spectra from a series of materials with different performance characteristics, or chemical imaging of a surface. Automation of some common imaging tasks, such as choosing which peaks to map or extracting spectra from regions of interest, is one of the key concepts to be presented.