Five classification techniques were compared for their accuracy in classifying normal, septicemic, and cadaver chicken carcasses, based on their optical reflectance spectra in the visible and near-infrared regions (504 - 888 nm). The techniques compared were the multiple- linear-regression, closest-class-mean, k-nearest-neighbor, artificial-neural-network (ANN), and principal-component/Mahalanobis-distance methods. The spectra were obtained with a diode array spectrophotometer system. The collection of the data and the development of the multiple linear regression model were described previously (Chen and Massie, 1992). The best results were obtained with the ANN model using the reflectances at the 8 optimal wavelengths identified by the multiple-linear regression method. The overall classification accuracy of this model was 91.6%. However, another ANN model with 192 inputs, which resulted in an overall accuracy of 90.4%, was preferred, because it utilized a broader range of reflectances (512.9 to 851.6 nm) without performing a wavelength search. This model yielded a 94.4% accuracy for the normal carcasses, 83.3% for the septicemic carcasses, and 94.3% for the cadaver carcasses.