Advances in camera trapping technology have enhanced data collection. In order to facilitate processing, computer programs such as Camelot have emerged facilitate the identification of animal species in a photograph and help organize them. Artificial intelligence makes it possible for us to take data analyasis one step ferder and identify an individual.. Our objective was to find a program that made it possible to identify lynx individuals. During the data collection, we followed the procedure prescribed by the "Lynx camera trapping guidelines", which was developed within the "Life Lynx" project. To process the data, we found two programs "Wilde ID" and "Wildbook". Wild ID in the newer version did not support the identification of individuals, and for the older version we know from experience that it was not of satisfactory quality. In the "Wildbook" program, we compared three groups of data. Tagged photos of identified lynxes (123 photos), photos of unidentified lynxes (577 photos) and unmarked photos of identified lynxes (27 photos). The "Hotspotter" algorithm, which is the basis for the analysis of lynx photos in the "Wildbook" program, calculates a special comparison coefficient for each comparison, which has prevented high-quality numerical comparison of data. The results were not satisfactory. The program recognized the lynx on the picture with great precision, even though there were more. The identification of lynx individuals has been less successful. The program placed pictures with weaker matches even when the pictures were given to the same lynx. The quality of the photograph is a major factor. The best comparisons were in photographs that were black and white, good sharpness and when the lynx was photographed from the side in the center of the photograph so that the whole individual could be seen. "Wildbook" program at this time and with our knowledge does not meet all the required criteria. The conclusion is that machine learning of this program needs to be further trained on our population in order for the program to show its full potential.