The advantages of optical wavelet transform used as a preprocessor for an artificial neural network are investigated. We show by digital simulation that this set-up can successfully identify and discriminate complex biometric images, such as fingerprints. The achieved capabilities include limited shift-, rotation-, scale- and intensity-invariance. We also show that the edges-enhancement filter, applied before the wavelet transform, significantly improves abilities of the system.