In the present paper, five different kinds of hypoglycemic tablets were identified using kernel principal component analysis (KPCA)-clustering analysis of their Raman spectra. KPCA was used to compress thousands of spectral data into several variables and to describe the body of the spectra before clustering analysis was chosen as further research method. The results showed that hypoglycemic tablets could be quickly classified using KPCA-clustering analysis. A disadvantage of Raman spectroscopy for this type of analysis is that it is primarily a surface technique. As a consequence, the spectra of the tablet core and its coating might differ. However, the KPCA-clustering analysis turned out to be a sufficiently reliable discrimination, i.e., 96% of the hypoglycemic tablets with coating and 100% of the hypoglycemic tablets without coating were predicted correctly. Overall, the Raman spectroscopic method in the present paper plays a good role in the identification and offers a new approach to the rapid discrimination of different kinds of hypoglycemic tablets