A platform technology is identified for grain handling facilities to improve grading and determine non-destructively different quality parameters of wheat. In this study, a near-infrared (NIR) hyperspectral imaging system was used to scan four wheat classes namely, Canada Western Red Spring (CWRS), Canada Prairie Spring Red (CPSR), Canada Western Hard White Spring (CWHWS), and Canada Western Soft White Spring (CWSWS) that were collected from across various growing regions in Manitoba, Saskatchewan, and Alberta in 2007, 2008, and 2009 crop years. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of four wheat classes at three moisture levels for each class was created. These image cubes were acquired in the wavelength region of 960-1700 nm with 10 nm intervals. Wheat classification was done using the non-parametric statistical and a four-layer back propagation neural network (BPNN) classifiers. Average classification accuracies of 93.1 and 83.9% for identifying wheat classes using the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), respectively, were obtained for two-class identification models that included variations of moisture levels, growing locations, and crop years of samples. In the pair-wise moisture discrimination study, near-perfect classifications were achieved for wheat samples which had difference in moisture levels of about 6%. The NIR wavelengths of 1260-1380 nm had the highest factor loadings for the first principal component using the principal components analysis (PCA). A four-layer BPNN classifier was used for two-class identification of wheat classes and moisture levels. Overall average pair-wise classification accuracies of 83.7% were obtained for discriminating wheat samples based on their moisture contents. Classification accuracies of 83.2, 75.4, 73.1%, on average, were obtained for identifying wheat classes for samples with 13, 16, and 19% moisture content (m.c.), respectively. Ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models were developed using a ten-fold cross validation for prediction. Prediction performances of PLSR and PCR models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Overall, PLSR models demonstrated better prediction performances than the PCR models for predicting protein contents and hardness of wheat.