Cytochrome P450 (P450) inhibition often occurs in a strongly substrate- and inhibitor-dependent manner, with a given inhibitor affecting the metabolism of different substrates to differing degrees and with a given substrate responding differently to different inhibitors. Traditionally, patterns of functional similarity and dissimilarity among substrates and inhibitors have been studied using clustering analysis of pair-wise correlation coefficients. Principal component analysis (PCA) is a widely used statistical technique that identifies the globally most significant independent trends in a set of data. Here, we show that PCA can be usefully applied to study the differential effects on a panel of P450 probe substrates by a panel of inhibitors, using published data on CYP3A4 (Kenworthy et al., 1999) and CYP2C9 (Kumar et al., 2006). PCA can detect functional similarities among substrates and inhibitors that are not readily apparent using pair-wise clustering analysis. PCA also allows identification of the functionally typical and atypical substrates that might be used in combination to fully explore the P450 functional landscape.