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Sensitive and accurate characterization of nanoparticle size in aqueousmatrices at environmentally relevant concentrations is still challenging for current nano-analysis techniques. Single particle inductively coupled plasma mass spectrometry (spICP-MS) is an emerging method to characterize the size distribution of nanoparticles and determine their concentrations. Herein for the first time the K-means clustering algorithm is applied to signal processing of spICP-MS raw data. Compared with currently used data processing approaches, the K-means algorithm improved discrimination of particle signals from background signals and provides a sophisticated, statistically based method to quantitatively resolve different size groups contained within a nanoparticle suspension. In tests with commercial Au nanoparticles (AuNPs), spICP-MS with the K-means clustering algorithm can quantitatively discriminate secondary "impurity-size nanoparticles," present at fractions of less than 2% by mass, from primary-size nanoparticles with the minimum resolvable size difference between the primary and secondary nanoparticles at ~20 nm. AuNP mixtures in which 80 nm particles act as the "primary size group" and 20 nm, 50 nm, or 100 nm particles act as the "impurity size group" were analyzed by spICP-MS, which reliably measured percentages of secondary impurity-size nanoparticles that are consistent with the expected experimentally determined values. Compared with dynamic light scattering (DLS), spICP-MS has remarkably better particle size resolution capability. We also demonstrated the size measurement advantage of spICP-MS over DLS for commercial CeO2 nanoparticles that are commonly used in the semiconductor industry, where quality control of the nanoparticle size distribution is critical for the wafer polishing process. [ABSTRACT FROM AUTHOR]