Objective: To identify mildew maize kernels accurately. Methods: A novel method to identify mildew maize kernels using spectral variables and color characteristics of hyperspectral images. Firstly, image segmentation, spectral variables and color features extraction were carried out on maize kernel images. Then, color features of maize kernel images were utilized to generate color histograms. Additionally, spectral variables and color histogram features were combined into a feature set. Finally, the distance functions were used to analyze the features in this feature set to identify mildew maize kernels. Results: For the proposed method, the maximum average identification deviation and accuracy for the mildew maize kernels were 1.12 and 97.59%, respectively. Compared with the method based on hyperspectral images+random frog+extreme learning machine, the method using hyperspectral images+colony optimization + BP neural network, and the method based on hyperspectral images+sparse auto-encoders + convolutional neural network, the identification accuracies of mildew maize kernels were significantly improved by the proposed method. Conclusion: The developed method can accurately identify whether the corn grain samples are mildew and the mildew degree of the maize kernel samples.