Recently, a considerable amount of research has been focused on inverse biometrics, that is, regenerating the original biometric sample from its template. In this work, the first reconstruction approach to recover hand geometry samples from their feature vectors is proposed. Experiments are carried out on the publicly available GPDS Hand DB, where the method has shown a remarkable performance, after reconstructing a very high percentage of the hands included in the dataset. Furthermore, the proposed technique is general, being able to successfully reproduce the original hand shape sample regardless of the information and format of the template used.