Mechanomyogram （MMG）-based pattern recognition refers to the process of collecting MMG bands and applying machine learning algorithms to perform motion recognition. To realize the real-time classification of sign language motions， six-channel MMG signals of three muscles on forearm are collected by dual-axis acceleration sensors which are controlled by lightweight embedded device with STM32 chip. The back propagation neural network （BPNN） algorithm is used to establish recognition classification models， where the parameters are extracted and put into the embedded system to transfer BPNN algorithm. The embedded device can accomplish real-time recognition of 30 kinds of sign language motions， with the self-test accuracy up to 99.6% and the accuracy of real-time recognition up to 97.5%. Moreover， the classification time for each motion is less than 0.52 ms， satisfying the real-time recognition condition. The results can be applied to the fields of rehabilitation engineering， sign language translator， and prosthetic control， etc.