Texture is an important visual property which has been largely employed for image characterization. Recently, Convolutional Networks has been the predominant approach on Computer Vision, and their application on texture analysis shows interesting results. However, their popularity steers around object recognition, and several convolutional architectures have been proposed and trained for this task. Therefore, this works evaluates 17 of the most diffused Deep Convolutional Neural Networks when employed for texture analysis as feature extractors. Image descriptors are obtained through Global Average Pooling over the output of the last convolutional layer of networks with random weights or learned from the ImageNet dataset. The analysis is performed under 6 texture datasets and using 3 different supervised classifiers (KNN, LDA, and SVM). Results using networks with random weights indicates...