Black bean (Phaseolus vulgaris L.) processing presents unique challenges because of discoloration, breakage, development of undesirable textures, and off-flavors during canning and storage. These quality issues strongly affect processing standards and consumer acceptance for beans. In this research, visible and near-infrared (Vis/NIR) reflectance data for the spectral region of 400-2,500 nm were acquired from intact dry beans for predicting five canning quality traits, i.e., hydration coefficient (HC), visual appearance (APP) and color (COL), washed drained coefficient (WDC), and texture (TXT), using partial least squares regression (PLSR). A total of 471 bean samples harvested and canned in 2010, 2011, and 2012 were used for analysis. PLSR models based on the Vis/NIR data showed low predictive performance, as measured by correlation coefficient for prediction (Rpred) for APP (Rpred = 0.275-0.566) and TXT (Rpred = 0.270-0.681), but better results for predicting HC (Rpred = 0.517-0.810), WDC (Rpred = 0.420-0.796), and COL (Rpred < 0.533-0.758). In comparison, color measurements from a colorimeter on drained canned beans showed consistently good predictions for COL (Rpred = 0.796-0.907). In spite of the low or relatively poor agreement among the sensory panelists as determined by multirater Kappa analysis (Kfree of 0.20 for APP and 0.18 for COL), a linear discriminant model using the Vis/NIR data was able to classify the canned bean samples into two sensory quality categories of "acceptable" and "unacceptable", based on panelists' ratings for APP and COL traits of canning beans, with classification accuracies of 72.6 % or higher. While Vis/NIR technique has the potential for assessing bean canning quality from intact dry beans, improvements in sensing and instrumentation are needed in order to meet the application requirements. © 2014 Springer Science+Business Media New York (outside the USA).