Nowadays, food security is essential in human life, especially for poultry meat. Therefore, the poultry raising is growing over years. This leads to the development of diseases on poultry, resulting in potentially great harm to human and the surrounding environment. It is estimated that when the diseases spread, the economic and environmental damages are relatively large. In addition, small-scale animal husbandry and an automated process to identify diseased chickens are essential. Therefore, this work presents an application of machine learning algorithms for automatic poultry disease identification. Here, the deep convolutional neural networks (CNNs) namely VGGNet and ResNet are used. The algorithms can identify four common diseases in chickens namely Avian Pox, Infectious Laryngotracheitis, Newscalte, and Marek against healthy ones. The obtained experimental results indicate that the highest achievable accuracies are 74.1 and 66.91 for VGGNet-16 and ResNet-50 respectively� The initial results showed positive results, serving the needs of the building and improving the model to achieve higher results. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.