Of all the long bones in the human skeleton, the bone fractured most often is the tibia. In the surgical treatment of shaft fractures, the use of the correct length nail is important. Therefore, the length of the tibia is crucial during orthopedic surgery and in forensic science, anatomy and anthropology. In this study, the Artificial Neural Network (ANN) method was applied to obtain a correct estimation of the tibia length from its proximal measurements. The inputs of the ANN, which are independent parameters of the problem, are the age of the subject, the tibia side, top measurement, middle measurement, bottom measurement and fibula length. A total of 193 tibia bone measurements were taken from an adult Turkish population. Five different input parameter combinations were tried for the correct determination of the tibia length. According to these combinations, the root mean square error (RMSE) values and correlation coefficients ® were