Grinding is widely used as a precision process for machining difficult-to-cut materials. Grinding productivity is still greatly dependent on the experience and skill of human operators. Focusing on the indirect method, an attempt was made to build up an intelligent system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The experiment indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole.