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Deep learning, neural networks and other data-driven processing techniques are increasingly used in the analysis of LiDAR point cloud data in forest environments due to the benefits offered in accuracy and adaptability to new environments. One of the downsides of these techniques in practical applications is the requirement for manually annotated data necessary for training neural networks, which can be time consuming and costly to attain. We develop an approach to training neural networks for forest tree stem segmentation from point clouds that uses synthetic data from a custom tree simulator, which can generate large quantities of training examples without manual human effort. Our tree simulator captures the geometric characteristics of tree stems and foliage, from which automatically-labelled synthetic point clouds can be generated for training a semantic segmentation algorithm based on the PointNet++ architecture. Using evaluations on real aerial and terrestrial LiDAR point clouds from a range of different forest sites, we demonstrate our synthetic data-trained models can out-perform, or provide comparable performance with models trained on real data from other sites or when available real training data is limited (increases in IoU from 1–7%). Our simulation code is open-source and made available to the research community. [ABSTRACT FROM AUTHOR]