In an interconnected power system, any sudden small load perturbation in any of therninterconnected areas causes the deviation of area frequency andtie line power. Automaticrngeneration control (AGC) plays a very important role to maintain system frequency and tie linernpower flow at their scheduled values during any load perturbation.Classical controllers like PIrnand PID controllers are very simple for implementation and give better dynamic response. But,rnthey have larger response time, lack of efficiency, poor handling of system nonlinearities and arernnot suitable for complex, uncertain, high order and time delay systems. Intelligent controllers canrnprovide a high adaption to changing conditions and have ability to make decisions quickly byrnprocessing imprecise information. They can also perform effectively even with nonlinearities.rnThis thesis work presents the application of an intelligent AGC such as artificial neural networksrn(ANN) and fuzzy logic controller (FLC) for a hydropower system. The ANN techniques are usedrnfor AGC of interconnected hydro power systems. The feed forward neural network controllersrnare developed and trained using Lavenberg-Marquardt (LM) back propagation algorithm underrnsupervised training method with adequate amount of data that are generated by using optimalrncontrol strategies.rnThe programming was done in MATLAB and the resultshows an improved performance in termsrnof rise time, settling time, steady state error and overshoot. The designed FLC gave a responsernwith settling time 45 sec and a frequency deviation of 2.3per unit (p.u). This is a better resultrnthan that of a PID controller with settling time and frequency deviation of 125 sec and 2.6p.urnrespectively. The designed ANN controller also gave a performance that is close to optimalrncontroller and superior to the integral controller as expected.