Determination of color values on tomato leaves stressed by the high temperature using hyperspectral imaging technique was studied in this paper. Hyperspectral images of sixty healthy and sixty unhealthy tomato leaves in the wavelengths of 380～1 023 nm were acquired by the hyperspectral imaging system. Simultaneously, three color parameters (L*, a* and b*) were measured by a colorimeter. Reflectance of all pixels in the region of interest (ROI) was extracted from the corrected hyperspectral image. Partial Least Squares (PLS) models were established based on different preprocessing methods. Successive Projections Algorithm (SPA) was identified to select effective wavelengths. Finally, Partial Least Squares-Discriminant Analysis (PLS-DA) models were built to classify different types of samples. The results showed that the determination coefficient (R2) were 0.818, 0.109 and 0.896 in the prediction sets of PLS modes; 0.591, 0.244 and 0.673 in the prediction sets of SPA-PLS models. The overall classification accuracy in the prediction sets of PLS-DA models were over 77.50%. It demonstrated that it is feasible to measure color values on tomato leaves and identify different types of samples using hyperspectral imaging technique.