This paper presents a solution for gender classification problem using GAL network which is a model of neural networks. This network is trained and tested on a publicly available Stanford dataset including 400 images (200 female and 200 male). Before classification process, primarily Principal Component Analysis (PCA) is applied on 128x128 pixel face images to reduce dimension and the dimension is reduced by about 99%; then GAL, an incremental neural network for supervised learning, is used to classify the data as male or female. Ten-fold cross validation is used for determining the accuracy rate of the classification. This paper has a novelty in the way of applying GAL to face images for gender classification. Test results show that 96% classification performance is obtained with GAL.