Aiming at the problem that high frequency signal receiving and storing function in motor bearing monitoring system is easy to lose data and how to realize accurate diagnosis of bearing faults, a motor bearing fault monitoring system is designed and developed by using LabVIEW, Access and MATLAB hybrid programming. Through the producer and consumer structure of LabVIEW, the system realizes the high-speed receiving and real-time saving of vibration signals by TCP/IP communication. Through LabVIEW UDL to achieve Access database add, delete, change, check operation; Aiming at the problem of bearing state pattern recognition, a bearing fault diagnosis method based on Variational Mode Decomposition combined with permutation entropy and Self-Organizing feature Map neural network was proposed. After experimental verification, the receiving speed of high frequency signal of motor bearing fault monitoring system reaches 12.577 KSps, which can realize real-time data access. The average recognition preparation rate of bearing fault diagnosis method based on VMD-PE-SOM neural network proposed in signal analysis function reaches 99.06%. The system integrates the function of vibration signal acquisition and fault diagnosis together, which has the advantages of fast receiving speed, no packet loss, good interaction and high fault recognition rate.