We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.