Segmentation of organs at risk (OARs) in medical images is an important step in treatment planning for patients undergoing radiotherapy (RT). Manual segmentation of OARs is often time-consuming and tedious. Therefore, we propose a method for automatic segmentation of OARs in thoracic RT treatment planning CT scans of patients diagnosed with lung, breast or esophageal cancer. The method consists of a combination of a 2D and a 3D convolutional neural network (CNN), where both networks have substantially different architectures. We analyse the performance for these networks individually and show that a combination of both networks produces the best results. With this combination, we achieve average Dice coefficients of 0.84± 0.05, 0.94± 0.02, 0.91± 0.02, and 0.93± 0.01 for the esophagus, heart, trachea, and aorta, respectively. These results demonstrate potential for automating segmentation of organs at risk in routine radiotherapy treatment planning.