Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF-SIMS data.Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF-SIMS peak lists using up-binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time-of-flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low-density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF-SIMS data.Artificial neural networks, in the form of self-organising maps, are introduced and provide a non-linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions.This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.