Painters can be affected during their life simultaneously from different movements, the same movements or a few different movements. This situation makes the problem of identifying, and classifying painters more difficult. In this paper, we have tested the latest deep neural networks on this problem. There are 17 painters who lived in different term and influenced by different art movements, an average of 46 paintings per painters in our data set to test this problem. GoogleNet, DenseNet, ResNet50, ResNet101 and Inceptionv3 networks are applied to this data set. Although DenseNet gives the highest result, considering the cost parameters such as training time and file size, the Inceptionv3 and ResNet50 which provide near to DenseNet results is the optimum networks. (C) 2019 The Authors. Published by Elsevier Ltd.