Automatic segmentation of anatomical structures in X-ray images is essential for clinical and research applications, particularly as the increasing volume of medical examinations necessitates workflow automation and efficient data screening. While numerous public X-ray imaging datasets exist, they are predominantly limited to chest X-rays, hindering AI-driven solutions for whole-body segmentation. In this paper, we propose a method for whole-body anatomical segmentation in X-ray images using synthetic data. We generate synthetic X-ray projections from an existing CT dataset using the DiffDRR framework and train five multi-class 2D UNet models, each targeting distinct anatomical groups. To assess generalization, we validate a subset of our models on two real X-ray imaging databases. Our models achieve a per-class median Dice Similarity Coefficient (DSC) above 0.88 for nearly 79 anatomical structures on the synthetic test set and perform on par with models trained on real data for rib segmentation with a similar architecture. We further highlight key challenges in transferring models from simulation to real-world datasets. Our models are made publicly available on GitHub ( github.com/risc-mi/totalsegmentator2D ) to facilitate further development.