This paper introduces a novel method for the generation of high-resolution synthetic hand-print images. Specific traits, such as fingerprint, palmprint, and hand-shape, are synthesized to obtain a whole hand-print. Each trait is generated by a methodology that mimics the nature of the corresponding biometric data and their main degrees of freedom. The biometric traits are then integrated into a single high-resolution realistic image. A quantitative validation of the obtained patterns is carried out in the context of minutiae matching by comparing genuine and impostor distributions between synthetic and real hand-prints. The proposed approach also proved to be useful for algorithm training/optimization.