Abstract: The color and tenderness of fresh chilled chicken were evaluated by near-infrared hyperspectral imaging system (900~1700 nm) combined with stepwise algorithm. By collecting hyperspectral images of fresh slaughtered chicken, extracting spectral reflectance information within the region of interests (ROI) of images of test samples, pretreating spectra with median filter (MFS), multivariate scattering correction (MSC) and standard normal variable transformation (SNV), respectively, the spectral data was mined by partial least square (PLS) and multivariate linear regression (MLR) to build the quantitative relationship between spectra and chicken color parameters (L^*, a^*, b^*) and tenderness. As a result, the PLS regression based on full 486 wavelengths (F-PLS) pretreated by MFS showed better performance in predicting L^* (R_P=0.904, RMSEP=2.036), b^* (R_P=0.908, RMSEP=1.577) and tenderness (R_P=0.948, RMSEP=1.596). The optimal wavelengths were then selected by stepwise algorithm to simplify the F-PLS models and improve the prediction efficiency. It was indicated that MLR model established with 14 optimal wavelengths selected from SNV spectra showed better performance in predicting L^* value (R_P=0.894, RMSEP=2.160). The O-PLS model based on 13 optimal wavelengths screened from SNV pretreatment spectrum had better performance in predicting b^* value (R_P=0.877, RMSEP=1.811). The O-PLS regression model based on 20 optimum wavelengths screened from MFS pretreatment spectrum showed better performance for predicting tenderness (RP=0.888, RMSEP=2.408 N).The overall results indicated that near-infrared hyperspectral imaging combined with stepwise algorithm had a great potential in fast evaluation of color (L^* and b^* value) and tenderness in chicken.