Radiologists are always looking for more reliable and robust methods to help them assess, describe and classify bone structures in x-ray images. Although, in the recent years, computer-assisted techniques have proven to be useful in this regard, they still face difficult challenges such as inter-subject variability in shape and a lack of contrast in the digitized images of radiographs. These challenges have focused the attention of the computer vision research community on techniques that employ deformable models. One such technique, i.e., Active Shape Models (ASM), has received significant attention due to its ability to capture the shape variability and to deal with the poor quality of the images in a straightforward manner. However, as is often the case with iterative optimization techniques, success of the ASM search step is highly dependent on the initial positioning of the mean shape on the target image. Within the specific framework of automatic, cervical vertebra segmentation, we have developed and tested an up-front preprocessing algorithm that estimates the orientation and position of the cervical vertebrae in x-ray images and leads to a more accurate, initial placement of the mean shape. The algorithm estimates the orientation of the spine by calculating parallel-beam line integrals of the x-ray images. The position of the spine is estimated by considering the density of edges perpendicular to the line integral that gives the estimate of the orientation. The output of the algorithm is a bounding box surrounding the cervical spine area. Morphometric points placed by expert radiologists on a set of 40, digitized radiographs were used to quantify the efficacy of the estimation. This test yielded acceptable results in estimating the orientation and the locating of the cervical spine.