In recent years, a considerable amount of methods have been proposed for detecting and reconstructing the spine and the vertebrae from CT and MR scans. The results are either used for examining the vertebrae or serve as a preprocessing step for further detection and annotation tasks. In this paper, we propose a method for reliably detecting the position of the vertebrae on a single slice of a transversal body CT scan. Thus, our method is not restricted by the available portion of the 3D scan, but even suffices with a single 2D image. A further advantage of our method is that detection does not require adjusting parameters or direct user interaction. Technically, our method is based on an imaging pipeline comprising five steps: The input image is preprocessed. The relevant region of the image is extracted. Then, a set of candidate locations is selected based on bone density. In the next step, image features are extracted from the surrounding of the candidate locations and an instance-based learning approach is used for selecting the best candidate. Finally, a refinement step optimizes the best candidate region. Our proposed method is validated on a large diverse data set of more than 8 000 images and improves the accuracy in terms of area overlap and distance from the true position significantly compared to the only other method being proposed for this task so far.