This paper proposes a hybrid opto-electronic method for the fast automatic verification of handwritten signatures. This method combines several statistical classifiers and consists of three steps. The first step aims to transform the original signatures using the identity and four Gabor transforms. For each image transform, the second step is to intercorrelate the analysed signature with the similarly transformed signatures of the learning database. Finally, the third step performs the verification of the authenticity of signatures by fusing the decisions related to each transform. Image transforms and intercorrelations can be computed in real time using a high-speed optical correlator. The different decisions and their fusion are then digitally performed. The opto-electronic implementation of the proposed method has been simulated on a large database, taking into account the specific constraints of the optical implementation. Satisfactory results have been obtained. Indeed, the proposed system allows the rejection of 62.4% of the forgeries used for the experiments when 99% of genuine signatures are correctly recognized.