Corn is easily infected by Aspergillus ochraceus， which is severely hazardous to human health. As the traditional methods for Aspergillus ochraceus detection are time-consuming and destructive， it is necessary to develop a rapid and non-destructive method for monitoring the growth of Aspergillus ochraceus in corn during storage. In this study， multispectral imaging technology combined with chemometric methods was used to obtain the optimal model for predicting the count of Aspergillus ochraceus quantitively and classifying the infection degree qualitatively. The results showed that compared with partial least square (PLS) and least square-support vector machine (LS-SVM)， back propagation neural network (BPNN) showed the best prediction performance with correlation coefficient of prediction (Rp) value of 0.9494， and the lowest root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) values of 2.6693 and 2.2743 CFU/g， respectively. In addition， for the classification experiment of the infective degree， BPNN was also the best prediction model with the accuracy of calibration(Ac) and the accuracy of prediction(Ap) both reached 100%. The results indicated that multispectral imaging combined with chemometric methods provided a promising technique to evaluate the infection of Aspergillus ochraceus in corn.