The e-nose system and Gaussian process (GP) classifier were used to accurately monitor physical and chemical changes in solid-state fermentation (SSF) of crop straws to replace off-line chemical analysis in laboratory. The SSF experiment cycle is seven days and the gas monitoring data sets were collected by e-nose every 24 hours. In this experiment 20 data sets corresponding to 20 batches of fermentation processes were collected, and ten of which were used for training GP classifier, while the rest for testing the performance of it. Test results show that the e-nose system could effectively monitor SSF process of crop straws and the classification accuracy of GP classifier was higher than that of support vector machine classifier or neural networks classifier. So the e-nose system combined the GP classifier method could be an effective strategy to monitor SSF process of crop straws.