Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this paper we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its r-principal subspace, the authenticity is then can be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forgery signatures. The results show remarkable level of accuracy in signature verification and significantly low Equal Error Rate (EER) in comparison to up-to-date online techniques.