This study introduces the application of near infrared spectroscopy (NIRs) to detect bunch withering disorder in date fruit (cv. Mazafati). The samples included intact as well as infected date fruits at different stages of ripening. Chemometric evaluation of the data was performed by soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), and principal components analysis combined with artificial neural networks (PCA-ANN). The PLS-DA algorithm was able to provide models with the best classification performance, followed by SIMCA and then PCA-ANN. The maturity stage of samples influenced the performance of the classification methods. The classification accuracy for the late harvested samples was better than those harvested at normal time and the combined data set in all classification analyses. The total accuracies of 82%, 93% and 86%, respectively for normal, late and combined data sets demonstrate that NIRs with PLS-DA has a strong potential to detect the bunch withering disorder in date fruit. (C) 2012 Elsevier Ltd. All rights reserved.