Abstract About 65 million people suffer from Chronic Obstructive Pulmonary Disease (COPD) and 3 million die from it each year, making it the third leading cause of death worldwide. Spirometry, pulmonary function test, chest x-ray, CT scan, bronchoscopy and lung biopsy are the methods used for evaluating the lung abnormalities. Therefore mentioned methods are expensive and long process where patients will undergo some pain and difficulties to perform these tests and also it not deal with lung sounds. Lung sounds are generated due to the flow of air through airways during the breathing cycle. The proposed methodology includes designing a low-cost module to acquire lung signals more easily and cheaply. This model includes a high gain sound detector with excellent noise cancellation features, data acquisition, and software. This is a sensing mechanism where bio sound signals of lung is captured. They are recorded from the chest region so mostly they will superimpose with heart signals. To separate the lung sound and heart sound, an FIR and a band-pass filter algorithm with a cut-off frequency 0.5 Hz is developed. To suppress the acquisition of the noise, a wavelet filter is designed and the lung sound is separated effectively. This prototype is tested against people of the age group 20–24 years including both genders with two different categories i.e. smoking and non-smoking population. The result showed a clear difference of higher intensity of sounds for smokers and low for non-smokers. This prototype test suggests that it can be a potential tool for detecting the abnormalities of lung with lung sounds.