Archival aerial photographs are a valuable source of information on the land cover. Unfortunately, these are singlechannel, monochromatic data, which means that the interpretation possibilities of these data are significantly limited. Therefore, a research was conducted in order to increase the possibility of detecting and identifying photographed elements of both natural and anthropogenic land cover. In this paper, a semi-automatic method of coloring archival photographs that uses image processing tools used in popular remote sensing software is presented. It is based on the segmentation, classification, pseudocoloring and pansharpening process. The tests were carried out on a set of aerial photos acquired in the 1950s, where mainly agricultural and forest areas with single rural buildings were photographed. Evaluation of the developed method was done through a visual analysis of the generated color images. The visual assessment was supplemented with a calculation of the value of the color accuracy index for each land cover class tested, i.e., forests, low vegetation, bare soils, water and anthropogenic objects. The presented method gives the opportunity to increase the visual quality of aerial images by giving them colors similar to natural ones while maintaining the level of detail. The visual enhancement of archival images, on the other hand, enables the automation of the identification process and analysis of photographed objects that have so far been performed manually only based on the interpreter's experience.