Synthetic-aperture radar (SAR) imagery has great potential for maritime surveillance with its global coverage as well as its weather independence. In order to leverage this potential, machine learning can be used to automatically process large amounts of data for different goals. This work focuses on the use of deep learning algorithms for ship classification. Particularly, the potential of transfer learning applied to convolutional neural networks (CNNs) is assessed in this context. This is especially relevant for tasks like this one, where there is no huge labelled dataset available for training. The aim is thus to see how to leverage knowledge from models pre-trained on other tasks (source tasks) and use them for ship classification (target task).