Forward Looking Sonars (FLS) are a typical choiceof sonar for autonomous underwater vehicles. They are mostoften the main sensor for obstacle avoidance and can be usedfor monitoring, homing, following and docking as well. Thosetasks require discrimination between noise and various classes ofobjects in the sonar images. Robust recognition of sonar data stillremains a problem, but if solved it would enable more autonomyfor underwater vehicles providing more reliable informationabout the surroundings to aid decision making. Recent advancesin image recognition using Deep Learning methods have beenrapid. While image recognition with Deep Learning is known torequire large amounts of labeled data, there are data-efficientlearning methods using generic features learned by a networkpre-trained on data from a different domain. This enables usto work with much smaller domain-specific datasets, makingthe method interesting to explore for sonar object recognitionwith limited amounts of training data. We have developed aConvolutional Neural Network (CNN) based classifier for FLS-images and compared its performance to classification usingclassical methods and hand-crafted features.