Electrical recordings of the heart sounds namely, the phonocardiogram (PCG) signals contain information regarding the heart condition of diagnostic importance. Characteristic features of PCG signals have been explored using several automatic detection algorithms to aid in disease diagnosis. A major limitation is that, many of these methods have been demonstrated only on PCG clean signals with limited test data that lacks variety to provide information of diagnostic importance. A more robust method to characterize PCG signal is required that can aid in discriminating normal and diseased heart conditions such as heart murmur etc. In this work, it was hypothesized that a multiscale frequency (MSF) analysis can discriminate normal PCG and PCG with murmur based on their varying frequency content. 13 samples of normal PCG and heart sound signal with murmur from Peter Bentley Heart Sounds Database sampled at 44.1 kHz were used for analysis. A 4th order Butterworth lowpass filter was designed 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 < 0.05. The mean MSF for normal PCG was 108.94±13.38 Hz and the mean MSF for murmur heart sound signal was 47.71±16.31 Hz. MSF was significantly different between normal and murmur sound signal with p < 0.01. Validation of this technique with larger dataset is required. MSF technique can discriminate normal PCG and murmur sound signal. The results motivate the analysis and comparison of normal PCG's with different cardiac conditions that can aid in disease diagnosis.