In recent years, parallel distributed processing has provided a new paradigm for algorithms, such as in missile guidance, which requires a high degree of computational efficiency as well as reliability and smaller size hardware. A problem of particular interest to the guidance literature is the closed-loop optical solutions that can be achieved on-board the missile. Furthermore, a desirable guidance scheme should be robust to low signal-to-noise conditions that generally arise in long-range applications. In this paper we shall present a neural network- based guidance scheme which provides a real-time optimal control on-board the missile with the inclusion of noise in the LOS angular rate data. The neural network is trained in an off-line session using optimal solutions obtained from an optimal control software resulting in a real- time closed-loop guidance method. The performance of the proposed scheme is then evaluated for different levels of SNR of the Line-Of-Sight (LOS) angular rate in a tail-chase engagement. In doing so, similar tests were conducted for the currently used closed-loop proportional navigation method and the potentially available technique of iterative optimal open-loop control with and without the presence of noise in the LOS angular rate. Although we did not include the noise in the missile/target dynamical model, the results indicate that the neural network-based scheme shows more robustness to low signal-to-noise situations as compared with traditional proportional navigation methods. This superiority is due, among other things, to the elimination of some of the restrictive, and in many cases unrealistic assumptions made in the derivation of most current guidance laws in use such as, for instance, unbounded control, simplified dynamics and/or aerodynamics, and non-maneuvering targets, to name a few.