Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources,with the relative contribution from each source varying over time and space.The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material.This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling,and critically compares biogeochcmical and physical tracers used in fingerprinting studies.Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe,Africa,Australia,Asia,and North and South America.The application of seven commonly used mixing models to two case studies from the US(North Fork Broad River watershed) and France(BIcone watershed) with local and global(genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original authors.We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins(using correction factors) and Hughes(relying on iterations involving all data,and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems. Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compared biogeochmical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data data explored using data from 41 catchments across Europe , Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US (North Fork Broad River watershed) and France (BIcone watershed) with local and global (genetic algorithm) optimization methods identified all outputs remained in the acceptable range of err or defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins (using correction factors) and Hughes (relying on iterations involving all data, and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems.