Modern multislice X-ray CT scanners provide high-resolution volumetric image data containing a wealth of structural and functional information. The size of the volumes makes it more and more difficult for human observers to visually evaluate their contents. Similar to other areas of medical image analysis, highly automated extraction and quantitative assessment of volumetric data is increasingly important in pulmonary physiology, diagnosis, and treatment. We present a method for a fully automated segmentation of a human airway tree, its skeletonization, identification of airway branches and branchpoints, as well as a method for matching the airway trees, branches, and branchpoints for the same subject over time and across subjects. The validation of our method shows a high correlation between the automatically obtained results and reference data provided by human observers.