Heart auscultation (the interpretation of heart sounds by a physician) is a fundamental component of cardiac diagnosis. It is, however, a difficult skill to acquire. In decision making, it is important to analyze heart sounds by an algorithm to give support to medical doctors. In this study, two feature extraction methods are comparatively examined to represent different heart sound (HS) categories. First, a rectangular window is formed so that one period of HS is contained in this window. Then, the windowed time samples are normalized. Discrete wavelet transform is applied to this windowed one period of HS. Based on the wavelet detail coefficients at several bands, the time locations of S1–S2 sounds are determined by an adaptive peak detector. In the first feature extraction method, sub-bands belonging to the detail coefficients are partitioned into ten segments. Powers of the detail coefficients in each segment are computed. In the second feature extraction method, the power of the signal in a window which consists of 64 samples is computed without filtering the HSs. In the study, performances of these two feature extraction methods are comparatively examined by the divergence analysis. The analysis quantitatively measures the distribution of vectors in the feature space.