Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multi-dimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, DEC, to assign an observation to the class (cluster) C that has minimum DEC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, pure marine), and we demonstrate application of the method to a 5-year record of retrievals from the spaceborne POLDER-3 (Polarization and Directionality of the Earth's Reflectances) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.