Unmixing of grain-size distributionswithmultivariate statistical analysis gives indications of themain sediment transport processes and related environmental conditions in an area. We performed end-member mixing analysis (EMMA) of sedimentological data from 912 terrestrial sediment samples collected in the Donggi Cona catchment, north-eastern Tibetan Plateau. Up to the present, this is the largest sedimentological dataset on the Tibetan Plateau. EMMA resulted in the characterisation of three end-members that explain 88% of the variance within the dataset. The end-members all represent aeolian deposits. The first end-member EM 1 represents very fine dune sediments that were deflated from a former lake system. EM 2 represents medium sand deposits that were blown out from playa and alluvial fan sediments. EM 3 represents fine loess(−like) sediments mainly found at higher elevations. Different transformations, adding of a fourth end-member and adding of up to 200 loess samples do not change the composition of the end-members, demonstrating the robustness of themodel. EMMA allows the synchronous interpretation of very large datasets, resulting in a general characterisation of sediment transport in a particular area. Performing EMMA on the dataset demonstrates the importance of aeolian transport in this part of the world.