Phonocardiogram (PCG) signals are electrical recording of <b>heart</b> <b>sounds</b> containing vital information of diagnostic importance. Several signal processing methods exist to characterize PCG, however suffers in terms of sensitivity and specificity in accurately discriminating normal and abnormal <b>heart</b> <b>sounds</b>. Recently, a multiscale frequency (MSF) analysis of normal PCG was reported to characterize subtle frequency content changes in PCG which can aid in differentiating normal and abnormal <b>heart</b> <b>sounds</b>. In this work, it was hypothesized that MSF can discriminate normal PCG signal compared to an artifact, PCG with extra systolic <b>heart</b> <b>sounds</b> and murmur based on their varying frequency content. Various samples of PCG with normal and abnormal <b>heart</b> <b>sounds</b> were obtained from Peter Bentley <b>Heart</b> <b>Sounds</b> Database sampled at 44.1 kHz for analysis. The signal was filtered using a 4th order Butterworth lowpass filter with cutoff frequency at 200 Hz to remove higher frequency noise and MSF estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p &amp;lt; 0.05. Results indicate that MSF successfully discriminated normal and abnormal <b>heart</b> <b>sounds</b>, which can aid in PCG classification with more sophisticated analysis. Validation of this technique with larger dataset is required.