This paper deals with the development of an automated face recognition system as for personal identification and verification where the recognition phase is implemented using neural network (recognition classifier) on low resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses learning algorithm to recognize the familiar faces. Neural network with multi-valued neurons for image recognition will be considered in the paper. Such a network with original architecture analyzes the phases of the Fourier spectral coefficients corresponding to the low frequencies. Quickly converged learning algorithm and huge functionality of multi-valued neurons allow getting 100% successful recognition of the different classes of images including the blurred and corrupted ones. Simulation results are presented on the example of face recognition.