Outlier diagnosis is a very important step in building near infrared calibration model. Data outlier includes spectral outlier and chemical value outlier. Mahalanobis' distance, ratio of spectral residual and spectral variable leverage test were used to evaluate sample spectral outlier. Cook's distance and the ratio of sample square error of chemical value and predict value to the mean square error of calibration set were used to test chemical value outlier. Three calibration models of protein content of 50 wheat samples, protein content of 90 corn samples and cyclohexane content of four compounds mixture were investigated. It is demonstrated that outlier test is very helpful for optimizing near infrared calibration model.