This paper proposed various feature extraction procedures to separate crackles and rhonchi of pathological lung sounds from normal lung sounds. The feature extraction process for distinguishing crackles and rhonchus from normal sounds comprises three signal-processing modules with the following functions: (1) f(min)/f(max) was the frequency ratio from the conventional technique of power spectral density (PSD) based on the Welch method. (2) The average instantaneous frequency (IF) and the exchange time of the instantaneous frequency were calculated by the Hilbert Huang transform (HHT). (3) The eigenvalues were obtained from the singular spectrum analysis (SSA) method. In the classification process, a support vector machine (SVM) was used to distinguish the crackles, rhonchus and normal lung sounds. The results showed that the selected features positively represented the characteristic changes in sounds. The PSD frequency ratio and the eigenvalues demonstrate higher classification accuracy (between 90% and 100%) than the calculations of average and exchange time of IF. The calculated features are extremely promising for the evaluation and classification of other biomedical signals as well as other lung sounds. (C) 2014 Elsevier Inc. All rights reserved.