Abstract: The human ilium bone, as an entity of the hip bone, represents a very complex morphological structure of irregular shape. Building accurate subject specific 3D model requires complete geometric morphometry of given bone. Therefore, it is necessary to define reliable anatomical landmarks as points which have unique and consistent positions at the each bone, defined by coordinate values. Due to the fact that the number of these point depends on the complexity of the shape, in the case of the ilium bone 15 bilateral landmarks are separated. Based on these landmarks 26 parameters are determined as linear distances. Using statistical approach it is possible to predict coordinate values for 11 landmarks, based on the values for 4 points whose positions are easy to determine. Input data in the form of coordinate values are taken from anatomical points, localized at the sample of 32 polygonal models of the ilium bone. The tools of descriptive statistic and regression analysis are used for establishing proper dependencies between coordinate values, which results in 33 mathematical equations (6 linear, 18 squared and 9 logarithmic). These results are statistically significant, due to the the value of variance R2 (up to 0.83511) and the p-value which is less than 0.01 for regression coefficients. Based on measured and predicted coordinate values it is possible to calculate values for all parameters, using an expression for distance between two points in 3D, in proper Graphical User Interface (GUI) developed for the purpose of this study. The results of study, tested on randomly chosen male hip bone, proved proper accuracy. Landmark-driven approach presented here allows simple and fast prediction of the subject specific morphometry as the first step in building 3D bone surface model.