Phonocardiogram (PCG) signals contain important prognostic and diagnostic information regarding heart health. Recently, several automatic detection algorithms have been explored to profile the characteristics of heart sounds to aid in disease diagnosis such as heart murmur, presence of extra heart sound such as extra systole etc. These methods are often limited in performance in presence of various noises and motion artifacts due to sensor movement during PCG recordings. A more robust method to characterize PCG is required that can aid in discriminating normal, artifact signals and diseased heart conditions. In this work, it was hypothesized that multiscale entropy (MSE) analysis can discriminate normal PCG and artifact sound signal based on their varying signal complexity. 10 samples of normal PCG and artifact sound signal 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 MSE estimation was performed on the filtered dataset using custom MATLAB software. Mann-Whitney test was performed for statistical significance at p < 0.01.The mean MSE for normal PCG was 0.04±0.015 and the mean MSE of the artifact sound signal was 0.1±.05. MSE was significantly different between normal and artifact sound signal with p = 0.0013 (p < 0.01). Validation of this technique with larger dataset is required. MSE technique can discriminate normal PCG and artifact sound signal. The results motivate the analysis and comparison of normal PCG's with different cardiac conditions that can aid in disease diagnosis.