Handwritten signature verification is a well-established and potential area of research with numerous applications such as commercial (e.g., Credit card, bank check verification etc.), government (e.g., National ID card, Driver's license, passport control etc.) and forensic (e.g., corpse identification) application. In this paper, we propose a new approach to deal with the problem of handwritten signature verification and forgery detection using data glove. The technique is based on linearly projecting the glove signature into a low-dimensional space, through the Singular Value Decomposition (SVD). The Euclidean distance between the different groups of singular values is used to measure the authenticity of the tried signatures. The reliability and efficiency of the proposed system against forgeries are tested and reported. A comparative analysis has also been shown for data gloves with 14, 5, and 4 sensors respectively. © 2006 IEEE.