The railway industry is investigating new ways to improve the safety and the performance of signalling functions (e.g., train localization) and automate other complex train functions, such as signal and sign recognition, obstacle detection, and track discrimination. Such tasks require the artificial perception of the railway environment through the data acquired from different types of sensors, including cameras, LiDARs, wheel encoders, GNSS receivers, and inertial measurement units. However, testing new algorithms and solutions that use such sensory data requires the availability of a large amount of labeled data, acquired in different scenarios and operating conditions, which are difficult to obtain in a real railway setting, due to strict regulations and practical constraints in accessing the trackside infrastructure and equipping a train with the required sensors. To cope with such difficulties, this paper presents a visual simulation framework able to generate realistic railway scenarios in a virtual environment and automatically produce a variety of labeled datasets from different types of emulated sensors, including cameras, LiDARs, and inertial measurement units. Such scenarios and datasets can be used for testing innovative algorithms, as well as for training and testing deep neural networks for a variety of tasks, as image segmentation, object detection, visual odometry, track discrimination, etc. The proposed framework is particularly relevant for the railway domain, considered the lack of similar datasets and the difficulty of reproducing critical situations in a real environment. A set of experimental results are reported to show the effectiveness of the proposed approach.