Respiratory sounds are of significance as they provide valuable information on the health of the respiratory system. Sounds emanating from the respiratory system are uneven, and vary significantly from one individual to another and for the same individual over time. In and of themselves they are not a direct proof of an ailment, but rather an inference that one exists. Auscultation diagnosis is an art/skill that is acquired and honed by practice; hence it is common to seek confirmation using invasive and potentially harmful imaging diagnosis techniques like X-rays. This research focuses on developing an automated auscultation diagnostic system that overcomes the limitations inherent in traditional auscultation techniques. The system uses a front end sound signal filtering module that uses adaptive Neural Networks (NN) noise cancellation to eliminate spurious sound signals like those from the heart, intestine, and ambient noise. To date, the core diagnosis module is capable of identifying lung sounds from non-lung sounds, normal lung sounds from abnormal ones, and identifying wheezes from crackles as indicators of different ailments.