In this paper, lung sounds recorded by electronic auscultation, were classified as healthy and pathological. Linear Predictive Coding coefficients, Mel Frequency Cepstrum Coefficients mean and standard deviation were used as features. Records are consists of two data sets containing different respiratory cycle. k-Nearest Neighbor classification algorithm used and the performance obtained were discussed in the conclusion to the case of using different data sets and different attributes.