Many classification algorithms have been implemented to differentiate between different pulmonary diseases. Recently, machine learning techniques have used for lung sound classification, and have particularly focused on deep neural networks, which appear advantageous with large training datasets. In this paper, intending to provide a fully automatic classification system, we propose an alternative representation of input data called Gammatonegrams. Our approach was implemented on two different deep neural network architectures - VGG16 and ResNets for pulmonary pathologies classification. The ICBHI database was chosen as input for pulmonary conditions classification into- healthy, chronic and non-chronic. The results show that the two architectures gave an accuracy of 67.97^% and 60.80% for VGG16 and ResNet-50 respectively. Our results provide initial evidence that in the gammatonegram based classification of pulmonary conditions, the deep neural networks, can achieve significant accuracy.