Until recently, Roentgen Stereophotogrammetric Analysis (RSA) required the manual definition of all markers using a high-resolution measurement table. To automate this tedious and time-consuming process and to eliminate observer variabilities, an analytical software package has been developed and validated for the detection, identification, and matching of markers in RSA radiographs. The digital analysis procedure consisted of the following steps: (1) the detection of markers using a variant of the Hough circle-finder technique; (2) the identification and labeling of the detected markers; (3) the reconstruction of the three-dimensional position of the bone markers and the prosthetic markers; and (4) the computation of micromotion. To assess the influence of film digitization, the measurements obtained from nine phantom radiographs using two different film scanners were compared with the results obtained by manual processing. All markers in the phantom radiographs were automatically detected and correctly labeled. The best results were obtained with a Vidar VXR-12 CCD scanner, for which the measurement errors were comparable to the errors associated with the manual approach. To assess the in vivo reproducibility, 30 patient radiographs were analyzed twice with the manual as well as with the automated procedure. Approximately, 85% of all calibration markers and bone markers were automatically detected and correctly matched. The calibration errors and the rigid-body errors show that the accuracy of the automated procedure is comparable to the accuracy of the manual procedure. The rigid-body errors had comparable mean values for both techniques: 0.05 mm for the tibia and 0.06 mm for the prosthesis. The reproducibility of the automated procedure showed to be slightly better than that of the manual procedure. The maximum errors in the computed translation and rotation of the tibial component were 0.11 mm and 0.24, compared to 0.13 mm and 0.27 for the manual RSA procedure. The total processing time is less than 10 min per radiograph, including interactive corrections, compared to approximately 1 h for the manual approach. In conclusion, a new and widely applicable, computer-assisted technique has become available to detect, identify, and match markers in RSA radiographs and to assess the micromotion of endoprostheses. This new technique will be used in our clinic for our hip, knee, and elbow studies.