In order to check the presence of spinal deformity in the early stage, orthopedists have traditionally performed on children a painless examination called a forward- bending test in school screening. In forward-bending test, mainly medical doctor checks to see if one shoulder is lower than the other. But this test is neither reproductive nor objective. Moreover, the inspection takes much time when applied to medical examination in schools. To overcome these difficulties, a moire method has been proposed which takes moire topographic images of human subject backs and checks symmetry/asymmetry of the moire patterns in a twodimensional way on visual screening. In this paper, we propose a new technique for automatic detection of spinal deformity from moire topographic images. In the first stage, once the original moire image is fed into computer, the middle line of the subject’s back is extracted on the moire image by employing the approximate symmetry analysis. Regions of interest are then automatically selected on the moire image from its upper part to the lower part. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. Displacement of local centroids and difference of gray value are calculated between the left-hand side and the right-hand side regions of the moire images with respect to the extracted middle line. Extracted 4 feature vectors (mean value and standard deviation from the each displacement) from the left-hand side and right-hand side rectangle areas are applied to train the Neural Network (NN), Support Vector Machines (SVMs). In the final stage, normal and abnormal cases are classified by NN and SVM. An experiment was performed employing 1,200 real moire images based on NN and SVMs, and classification rates of 90.3% and 85.3% was achieved respectively.