Estimating the distribution and status of animal populations is crucial in various fields of biology. Monitoring species via their tracks is controversial due to unreliable recording techniques, manipulator bias and substrate variation. Furthermore, subjective identification of the foot that produces each track can lead to significant errors, for example, when assigning tracks made by different feet from the same individual to different individuals. The aim of this research was to develop an accurate, consistent and objective algorithm to identify the anteroposterior (hind/front) and mediolateral (right/left) position from digital threedimensional (3D) models of African lion (Panthera leo) paws and tracks using geometric morphometrics. We manually positioned 12 fixed landmarks on 132 paws and 182 tracks recorded in 3D using digital close-range photogrammetry. We used geometric morphometrics to evaluate and visualize the shape variation between paws and between tracks along the anteroposterior and mediolateral axes, and between paws and tracks. The identification algorithm using linear discriminant analysis with jack-knifed predictions reached a maximum accuracy of 95.45% and 91.21% for paws and tracks, respectively. We recommend the use of this objective position identification algorithm in future studies where tracks are compared between individual African lions.