A methodology for optimum sampling frequency selection for wavelet feature extraction is presented. We show that classification accuracy is enhanced by adequately selecting the parameters: number of decomposition levels, wavelet function and sampling rate. A novel approach for selecting the parameters based on particle swarm optimization (PSO) is presented. Experimental results conducted on two different datasets with support vector machine (SVM) classifiers confirm the superiority and advantages of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of accuracy rate.