Abstract: In order to conduct the online monitoring and fault diagnosis of the countershaft bearing of cutoff blade carrier in cigarette maker, an intelligent fault diagnosis method was established with SVM on the basis of frequency domain indexes after analyzing the influences of statistical indexes in time domain and frequency domain on SVM classification results. Testing was conducted on the FAG 2206-2RS-TVH bearings with prefabricated faults, the vibration data collected from the bearings with faults of different types and degrees were subjected to preprocessing, characteristic extraction, SVM intelligent training and fault diagnosis testing. The results showed that:1) At different rotational speeds and fault degrees, the SVM classification based on frequency domain statistic indexes presented higher accuracies of 92% and over, it was able to classify the status of bearings within a specified rotational speed range at different fault degrees. 2) The accuracy based on time domain statistic indexes was 77.6% compared to that based on frequency domain statistic indexes of 95.6%, which suggested that the later was more accurate. This method provides a support for the intelligent fault diagnosis of bearing in cigarette maker.