The power electronic researchers and designers are involved since last two decades in designing converter topologies for their effective use in high power applications. Recently, developed SAB converter being an advanced topology is gaining popularity because of its many attractive features. Despite of its attractive characteristics and merits it faces a serious problem of the generation of oscillations. These oscillations create disturbances in the systems network and also badly affect the performance of the converter itself. Such topologies with conventional controllers are also produce power quality problems such as; variation in frequency, voltage dips and swells, which lead to interruptions and transients in system networks. As it is a well known fact, that SAB converter is a variable structure system due to its switching operation and contains nonlinearities. Because of the high switching speed of SAB converter, i.e. in nano seconds, it is difficult to control it with linear controllers. In previous research the Proportional plus Integral (PI) controllers were used which were unable to control the SAB converter under nonlinear situations and ultimately this will lead to undesired results. Considering the deleterious effects and limitations of the conventional controllers in this research the Neural Network based controller is designed to handle the non-linear situations. Neural Network Controller (NNC) is recently found various applications in power electronic converters because of its good dynamic behavior and robustness. Designing of the Neural Network Controller for SAB converter is the main contributions in this research work. The NNC type cascade controller for SAB converter is successfully developed in MATLAB. The performance of single active bridge topology with NNC is tested under steady state, transient and dynamic region by considering the, line, duty ratio and reference voltage variations. Simulation results show improvements in transients and reference variation in the range of 26% to 38% respectively and 6 time improvement in line variation and 30 time improvement in duty ratio variation. The designed controller after through analysis, testing and comparison with previous designed controllers depicts that the neural network based controller is quite effective, efficient and has good transient and steady-state properties. Keywords: DC/DC converters, Single-Active Bridge converter, Neural Network Controller, Feed Forward Neural Network.