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Murmurs are auscultatory sounds produced by turbulent blood flow in and around the heart. These sounds usually signify an underlying cardiac pathology, which may include diseased valves or an abnormal passage of blood flow. The murmurs are classified based on their occurrence in different parts of the heart cycle; systolic murmurs and diastolic murmurs. This paper investigates features derived from cepstrum of the heart sound signals and use them to train three classifiers; k-nearest neighbor (kNN) classifier, multilayer perceptron (MLP) neural networks and support vector machines (SVM) for classification of heart sounds into normal, systolic murmurs and diastolic murmurs. These features have been compared with features extracted from short-term Fourier transform (STFT) and discrete wavelet transform (DWT) in combination with the above three classifiers. The classification experiments were carried out on the heart sounds samples collected from various Web sources. Among various combinations of the above features and classifiers, SVM trained on cepstral features are most promising for murmur classification with an accuracy of around 95%.