Esophagitis is divided into four grades according to the progress degree of disease by the LA classification method. This research was carried out on image processing with endoscope images for quantifying the four grades under the LA Classification. In a previous paper, which presented our work, the algorithm for detecting abnormal parts from one image was developed. This paper was conducted to classify esophagitis grade of one image itself. Whole 30 images were used in an experiment and included normal images and abnormal images with four grades. GLCM (gray level co-occurrence matrices) factors were extracted. The distributions of the texture image histogram were analyzed from each image for texture images. The algorithm to determine esophagitis grade used BPN (Back propagation network) that was composed of the texture histogram distribution for input data. It learned 20 images and verified with 10 images to diagnose under the LA classification system. Recognition ratio of learning result was 93.0% and verification result 77.0%. With features of the neural network, the success rate could be improved with this result by learning the data which were errors. Consequently, the recognition success rate appeared at 96% by total re-learned 30 images in addition to 10 images.