Features of male/female facial images are examined by analyzing the distribution of connection weights of the hidden-layer unit and the input-layer unit of a neural network that responds to their distinction. Twenty-five graylevel (black-and-white) facial images were used for each of the male and female samples. Several different numbers of mosaic blocks were used for the whole face region and each facial component region. The training parameters of the neural network and the minimum number of hidden layers that can discriminate male/female were obtained by a genetic algorithm. The neural network consisting of these parameters has a stable convergence in any region so that this can discriminate male/female with 100% accuracy. In the feature extraction experiments, hidden layers were obtained, which significantly respond to male/female facial regions. The facial features of male/female can be extracted by analyzing the connection weights between the hiddenlayer unit and the input-layer unit. The facial features extracted by using the proposed method were similar to those obtained by other psychological experiments and facial measurements.