The aim of this work is to explore the performance of active appearance models (AAMs) in reconstruction and interpretation of bones in hand radiographs. AAM is a generative approach that unifies image segmentation and image understanding. Initial locations for the AAM search are generated by an exhaustive filtering method. A series of AAMs for smaller groups of bones are used. It is found that AAM successful reconstructs 99% of metacarpals, proximal and medial phalanges and the distal 3 cm of radius and ulna. The rms accuracy is better than 240 microns (point-to-curve). The generative property is used (1) to define a measure of fit that allows the models to self-evaluate and chose between the multiple found solutions, (2) to overcome obstacles in the image in the form of rings by predicting the missing part, and (3) to detect anomalies, e.g. rheumatoid arthritis. The shape scores are used as biometrics to check the identity of patients in a longitudinal study. The conclusion is that AAM provides a highly efficient and unified framework for various tasks in diagnosis and assessment of bone related disorders.