This study investigates the suitability of multivariate techniques, including principal component analysis and discriminant function analysis, for analysing polycyclic aromatic hydrocarbon and heavy metal-contaminated aquatic sediment data. We show that multivariate "fingerprint" analysis of relative abundances of contaminants can characterize a contamination source and distinguish contaminated sediments of interest from background contamination. Thereafter, analysis of the unstandardized concentrations among samples contaminated from the same source can identify migration pathways within a study area that is hydraulically complex and has a long contamination history, without reliance on complex hydrodynamic data and modelling techniques. Together, these methods provide an effective tool for drinking water source monitoring and protection.