2D- and 3D-based innovative methods for surgical planning and simulation systems in orthopedic surgery have emerged enabling the interactive or semi-automatic identification of the clinical landmarks (CL) on the patient individual virtual bone anatomy. They enable the determination of the optimal implant sizes and positioning according to the computed CL, the visualization of the virtual bone resections and the simulation of the overall intervention prior to surgery. The virtual palpation of CL, highly dependent upon the examiner's expertise, was proved to be time consuming and to suffer from considerable inter-observer variability. In this article, we propose a fully automatic algorithmic framework that processes the pelvic bone surface, integrating surface curvature analysis, quadric fitting, recursive clustering and clinical knowledge, aiming at computing the main parameters of the acetabulum. The performance of the method was evaluated using pelvic bone surfaces reconstructed from CT scans of cadavers and subjects with pathological conditions at the hip joint. The repeatability error of the automated computation of acetabular center, size and axis parameters was less than 1 mm, 0.5 mm, and 1.5°, respectively. The computed parameters were in agreement (<1.5 mm; <0.5 mm; <3.0°) with the corresponding reference parameters manually identified in the original datasets by medical experts. According to our results, the proposed method is put forward to improve the degree of automation of image/model-based planning systems for hip surgery.