Objectives: Human lung sounds are important indicators of underlying lung pathology. The prime objective of this work is to classify normal and adventitious lung sounds in paediatric population using spectral features and artificial neural networks. Material and Method: 3M Littmann 3200 electronic stethoscope was used to record the lung sounds. After pre-processing ten spectral features were extracted. For classification, comparative performance of different artificial neural networks is studied and GFF neural network with calculated optimum parameters is selected. Results: For testing data Out of 49 normal subjects 48 were classified successfully and out of 52 pathological subjects 48 were classified successfully. The classification sensitivity and specificity obtained is 92.30% and 97.95% respectively. Conclusion: Early diagnosis of lung disorder is important especially in childhood so that further progress of the disease could be prevented. New approach to detect adventitious lung sounds is being proposed utilizing electronic stethoscope as a recording device. Combination of spectral features and artificial neural networks has provided classification accuracy of 95.12%.