Auscultation of pulmonary sounds provides valuable clinical information but has been regarded as a tool of low diagnostic value due to the inherent subjectivity in the evaluation of these sounds. In this work, a Digital Signal Processor is used to design an instrument capable of acquiring, parameterizing and subsequently classifying lung sounds into two classes with an aim to evaluate them objectively in real time. The instrument operates on sound signal from a chest microphone and flow signal from a pneumotachograph. The classification is carried out separately on the 12 reference libraries (pathological and healthy) of six sub-phases of a full respiration cycle and the results are combined to arrive at a final decision. The k-nearest neighbour and minimum distance classifiers with different distance metrics have been implemented in the instrument. The instrument was tested in the clinical environment, attaining 96% accuracy in real-time classification.