The study of weather modification must exactly identify the phases of cloud hydrometeor particles to improve the weather modification performance. The vehicle-borne X-band dual-polarization radar system set up by the Laboratory of Cloud-Precipitation and Severe Storms (LACS), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, can provide several dual-polarization radar observables, including radar reflectivity, differential reflectivity, specific differential propagation phase, and correlation coefficient, which are related to the sizes, shapes, and phases of hydrometeor particles. In this paper, the four polarimetric observables combined with environmental temperature are considered as five input parameters, and a fuzzy logic algorithm for hydrometeor particle identification is developed and implemented to discriminate ten different hydrometeor types including drizzle, rain, wet graupel, dry graupel, small hail, large hail, rain and hail mixture, wet snow, dry snow, and ice crystals. The identification algorithm is tested and estimated by using the radar data observed in southern and northern China, and comparing the results with the surface field observation and airborne instrument observations. The classification results indicate that the fuzzy logic algorithm is reasonable and practicable.