Since the beginning of the recording of lung sounds, many studies have been done on automatic recognition of lung sounds. But, multi-channel recording methods were frequently used in these studies. Whereas single-channel recording methods are more suitable for the auscultation procedure. Therefore, there is a greater need in the literature for studies on single-channel lung sounds. In this study, appropriate features derived from the Mel Frequency Cepstrum Coefficients (MFCC) and classification methods were searched for automatic classification of single channel common lung sounds. In the feature extraction phase, the Hjorth parameters, mean, standard deviation, skewness, kurtosis and entropy values were derived from the MFCC. The features were tested by using Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Linear Discriminant Analysis (LDA) and Naive Bayes (NBA) algorithms. The results obtained showed that the derived features from MFCC reached 99.05% success when the SFS method was applied.