In this study, the development of an algorithm for the classification of heart sound phonocardiogram waveforms such as Normal, Murmur, Extrasystole, Artifact. By presenting the approach used for classification from a general machine learning application point of view, the types of classifiers used were detailed by comparing their features and their performance. The Long-Short Term Memory method which supports the classification of each cardiac cycle in sound recordings. In addition to the LSTM-based features, our method incorporates spectral features to summarize the characteristics of the entire sound recording.