The classification of Raman spectra can be very useful in a wide range of diagnostic applications including bacterial identification. Before any form of classification can be carried out on the Raman spectra, some form of pre-processing is commonly applied. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose using support vector machines with the correlation kernel. The use of the correlation kernel on Raman spectra has not been presented before in any published work. Our results illustrate that the correlation kernel is ‘self-normalizing’ and produces superior classification performance with minimal pre-processing, even on highly noisy data obtained using inexpensive equipment. Such effective classification approaches can lead to clinically valuable diagnostic applications of Raman Spectroscopy. Copyright © 2010 John Wiley & Sons, Ltd.