Abstract: Numerous physical diseases come with age, many of which are inherited and can be alleviated through appropriate therapies if they are recognized in the early stage. The vertebral column supports the head and trunk; it enables us to stand erect, protects the nerves governing physical activities and sensations and is the main transfer passage of nerves. This study employed the artificial neural network and rough set theory to conduct experimental tests on the medical diagnosis of the vertebral column from University of California at Irvine (UCI) database. To avoid long operation time and large weight differences, the data were simplified by standardization and classification accuracy and area under receiver operating characteristic curve were used to assess diagnostic performance. This study used back-propagation neural network (BPNN), rough set theory GA Algorithm (RST-GA) and rough set theory Johnson Algorithm (RST-JA) to test a medical database of vertebral columns. The experimental results showed that the BPNN is superior to RST-GA and RST-JA and has higher accuracy on medical diagnosis of vertebral column. The classification accuracy and under the ROC curve (AURC) of BPNN were 90.32 and 99.42%, respectively. This study developed intelligent diagnostic methods to assist doctors on assessing vertebral column patients, thus, providing patients with more suitable and earlier medical care. With high diagnostic accuracy, the experimental results showed that BPNN classification prediction model was reliable on assessing the disease of vertebral column to facilitate a better lifestyle in a timely fashion for patients. However, without practical verification, this prediction model may not be applicable to other diseases.