Automatic identification of frontal (posteroanterior/anteroposterior) vs. lateral chest radiographs is an important preprocessing step in medical imaging. A recent approach by Amura et al. (Procs SPIE 2002; 4684: 308-315) is based on manual selection and combination of about 500 radiographs to generate as much as 24 templates by pixel-wise summing up the references, and a correctness rate of 99,99 % is reported. In order to design a fully automated procedure, 1,867 images were arbitrarily selected from clinical routine as reference for this work: 1,266 in frontal and 601 in lateral view position. The size of the radiographs varies between 2,000 and 4,000 pixels in each direction. Automatic categorization is done in two steps. At first, the image is reduced substantially in size. Regardless of the initial aspect ratio, a squared version is obtained, where the number h of pixels in both directions is a power of two. In the second step, the normalized cross correlation function at the optimal displacement is used for 5-nearest-neighbor classification. Leaving-one-out experiments were performed for h = 4, 8, 16, 32, and 64 resulting in mean correctness of 92.0 %, 99.3 %, 99.3 %, 99.6 % and 99.4 %, respectively. With respect to the approach of Amura et al., these results show that the determination of the view position of chest radiographs can be fully automated and substantially simplified if the correlation function is used directly for 5-NN classification.