In this paper, two intelligent adaptive controllers for milling processes are proposed. One is an intelligent adaptive controller with optimization (IACO) developed based on a neural network and genetic algorithm. The other is an intelligent adaptive controller with constraints (IACC) developed based on a neural network and expert rules. In the IACO, a modified back-propagation neural network (MBPNN), in which a dynamic factor is attached and the learning rate can be adjusted in the learning process is used for the online modelling of the milling system. In addition, a modified genetic algorithm (MAG), in which the search domain can be adjusted in every generation is used for the real-time optimal control of the milling process. In IACC, a simplified BP algorithm is used to learn online, the reverse function of the milling system and realize the real-time adaptive control in the milling process; some expert rules are combined in the BP neural network controller so as to ensure the reliability and stabilit...