Dried soybean is among the most popular snack foods consumed in numerous countries, and its quality has received considerable attention from processors and consumers. Color and moisture content are two critical parameters used to evaluate dried soybean quality. This study thus aimed to develop regression models for predicting the color and moisture content of soybeans simultaneously during the drying process using a hyperspectral imaging technique. Hyperspectral reflectance images were acquired from fresh and dried soybeans over the spectral region between 400 and 1000 nm for 270 samples. After the automatic segmentation of soybean images at each wavelength based on an active contour model, mean reflectance and image entropy parameters were extracted and tested separately and in combination for predicting the color and moisture content of the processed soybeans. Predicting models were built using the partial least squares regression method. Better prediction results for both color and moisture content were achieved using the mean reflectance data (with correlation coefficients or RP = 0.862 and root-mean-square errors of prediction or RMSEP = 1.04 for color, as well as RP = 0.971 and RMSEP = 4.7% for moisture content) than when using entropy data (RP = 0.839 and RMSEP = 1.14 for color, as well as RP = 0.901 and RMSEP = 9.2% for moisture content). However, the integration of mean reflectance and entropy data did not show significant improvements in predicting the color or moisture content. Overall, a simple hyperspectral imaging technique involving rapid image preprocessing and single spectral features showed significant potential in measuring the color and moisture content of soybeans simultaneously during the drying process.