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Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was …