Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset.