The gyro-mirror line-of-sight stabilization platform used to maintain the line-of-sight of electro-optical sensors mounted on moving vehicles is a multivariate and highly nonlinear system. The system is also characterized by a peculiar phenomenon in which a movement about one axis will trigger off a coupled movement in the other axis. Furthermore, uncertainties such as noise, practical imperfections and additional dynamics are often omitted from the mathematical model of the system thus resulting in a non-trivial control problem. In order to handle the complex dynamics of the gyro-mirror as well as to optimize the various conflicting control objectives, a multi-objective artificial immune system framework which combines the global search ability of evolutionary algorithms and immune learning of artificial immune systems is proposed in this chapter for the design of the gyroscope recurrent neural network controller. In addition, a new selection strategy based on the concepts of clonal selection principle is used to maintain the balance between exploration and exploitation of the objective space. Simulation results demonstrate the effectiveness of the proposed approach in handling noise, plant uncertainties and the coupling effects of the cross-axis interactions.