In this paper, we present a comparison of performance for different convolutional neural networks (CNN) for automatic classification of corrosion and coating damages on bridge constructions from images. Image recordings were taken during inspections. Through manual categorization and data augmentation, a total of 9300 images were collected and divided into five classes. Four different CNNs were trained using transfer learning in MATLAB. We have evaluated test performance through the metrics recall, precision, accuracy and F1 score. Test performance was also evaluated on damage detection accuracy, meaning how well the networks detect images that contain a damage. The convolutional neural network trained using VGG-16 had the overall best performance results, with average recall, precision, accuracy and F1 score being 95.45%, 95.61%, 97.74% and 95.53%, respectively. In the category of overall damage detection AlexNet performed best with 99.14% accuracy. The obtained results are promising, and make it possible to conclude that CNNs have a great potential in bridge inspections for automatic analysis of corrosion and coating damages.