Superheater temperature control is one of the most discussed problems of a power plant. Traditional PID controllers fail to provide a consistent performance in this control due load dependence of the controlled parameter and large delays involved. With the increasing use of DCS in power plants, it is possible to find solution to control problems using advanced algorithms like state feedback, fuzzy logic, neural networks etc. In this project an effort has been made to evaluate different advanced algorithm like State variable control and fuzzy logic based controllers and evaluate their performance against PID controllers. First a model of superheater is fitted from process reaction curve. For this model, PID controllers using three different algorithms are used including the classical Ziegler Nichols method. Then an observer is designed for state variable feedback. Finally fuzzy gain scheduling is designed. Simulations are done for setpoint changes and disturbances occurring at different locations. The result of simulations of different controllers are compared with standard performance measures.