Femora are a well preserved section of the skeleton after death. Therefore, they are commonly used in the field of forensic sciences, physical anthropology and anatomy. In addition, femur morphometry is helpful in finding sex or side (left or right) differences. The femur also shows characteristics of certain populations. Femur length is important for calculation of individual stature. In this study, the artificial neural network method was used to estimate femur length. In total, 230 femora exemplar were used. The three input parameters of the method were the distance between trochanter major top point and trochanter minor bottom point, the diameter of caput femoris and the diameter of collum femoris. By using these parameters, the artificial neural network estimation on femur length was performed. The results show that the method is capable of performing this estimation. In addition, sex discrimination was performed and achieved with 82% accuracy. As well as the identification of sex or side differences, morphometry of the proximal femur is necessary and important for surgical procedures.