A typical imaging mass spectrometry data set can contain 100+ images, each describing the distribution of a specific biomolecule. Multivariate and hierarchical clustering techniques have been developed to investigate the correlations within a data set, and have revealed the differential patterns associated with different organs/anatomical features. These methods do not quantify the correlations between the hundreds of molecular distributions produced in an imaging mass spectrometry experiment, and are extremely difficult to apply to multiple tissue section investigations. This latter aspect includes quantifying the correlation between the results of repeat imaging mass spectrometry experiments, a crucial aspect for determining the significance of any measured changes in distribution. To date, the large chemical background and pixel-to-pixel variation in the images has limited the quantification of correlation between imaging mass spectrometry results. Here, we demonstrate how to quantify the correlations between imaging mass spectrometry images, both within a data set and between data sets.