The invention discloses a neural network-based grinding machining working condition detection method, which comprises the following steps of: establishing a neural network model; acquiring field acoustic emission information under a standard working condition by using a sensor information acquisition module, inputting a sample library consisting of the acquired acoustic emission data and standardworking condition data into a neural network learning module, obtaining a weight threshold parameter by using an error back propagation algorithm and outputting the weight threshold parameter to a neural network operation and output module; according to a received real-time data vector, performing an operation and outputting a real-time working condition vector by using the neural network operation and output module; and judging the conditions of a cutter and a work piece according to definitions of each component in the working condition vector. The method has the advantages that: by utilizing a learning and intelligent judgment function of a neural network, the machining conditions of the work piece and the cutter can be automatically judged, dependence on specialized workers is greatlyreduced and machining efficiency is improved, artificial judgment factors can be effectively reduced, machining efficiency and machining quality are improved and unnecessary damages to the work pieceand the cutter are avoided.