Pneumonia and asthma are the common diseases in pediatric population. The diseases share some similarities of symptoms that make them difficult to separate without the proper diagnostic tools. The majority of pneumonia cases occur in the third world countries wherein even the basic diagnostic tools (e.g.: x-ray) are extremely rare. In these countries, the WHO recommends using rapid breathing and chest in-drawing as approach to diagnose pneumonia in children with cough. As the results, many asthma patients were misdiagnosed as pneumonia and prescribed for unnecessary antibiotic treatment. In this study, we propose a cough sound analysis based method to differentiate pneumonia from asthma. Cough is the major symptom of pneumonia and asthma. Past studies showed the acoustic of cough sounds may carry important information related with the diseases. However, there were no attempts to use cough sounds to separate pneumonia and asthma in pediatric population. Our method extracted sound features such as Mel-frequency cepstral coefficients, non-Gaussianity score and Shannon entropy. The features were then used to develop artificial neural network classifiers. Tested using leave one out validation technique in eighteen subjects, our method achieved sensitivity, specificity and Kappa of 89%, 100%, and 0.89 respectively. The results show the potential of our method to be developed as a tool to differentiate pneumonia from asthma in remote areas.