The abundance of plastic products in modern society has resulted in a proliferation of small plastic particles called "microplastics" in the global environment. Currently, spectroscopic techniques such as Fourier-transform infrared and spontaneous (i.e., conventional) Raman spectroscopy are widely employed for the identification of the plastic microparticles, but these are rather time consuming. Stimulated Raman scattering (SRS) microscopy, based on the coherent interaction of 2 different laser beams with vibrational levels in the molecules of the sample, would enable much faster detection and identification of microplastics. Here, we present for the first time an SRS-based method for identifying 5 different high production-volume polymer types in microplastics extracted from environmental or consumer product samples. The particles from the extracts were collected on a flat alumina filter, and 6 SRS images were acquired at specifically chosen wavenumbers. Next, we decomposed these spectral data into specific images for the 5 polymers selected for calibration. We tested the approach on an artificial mixture of plastic particles and determined the signal-to-noise and level of cross talk for the 5 polymer types. As a proof of principle, we identified polyethylene terephthalate particles extracted from a commercial personal care product, demonstrating also the thousand-fold higher speed of mapping with SRS compared with conventional Raman. Furthermore, after density separation of a Rhine estuary sediment sample, we scanned 1 cm2 of the filter surface in less than 5 hr and detected and identified 88 microplastics, which corresponds to 12,000 particles per kilogram dry weight. We conclude that SRS can be an efficient method for monitoring microplastics in the environment and potentially many other matrices of interest.