This paper describes an approach for feature extraction in speech recognition systems using kernel principal component analysis (KPCA). This approach consists in representing speech features as the projection of the extracted speech features mapped into a feature space via a nonlinear mapping onto the principal components. The nonlinear mapping is implicitly performed using the kernel-trick, which is an useful way of not mapping the input space into a feature space explicitly, making this mapping computationally feasible. Better results were obtained by using this approach when compared to the standard technique.