The aim of hydro system scheduling problem is to find out the periodic water releases from each reservoir and through each power house so as to optimize the total benefit of hydro generated energy. The major focus of the work contained here is to develop and explain methods for solving different types of problems concerning optimal operation of interconnected hydro power plants. A decomposition method is explained which optimizes the mid-term operation of a practical reservoir system. Also a hybrid method based on decomposition and two-phase neural network is discussed to solve this problem. The objective here is to maximize annual hydro-generated energy. Effective solution technique based on neural nets is elaborated to solve the multiobjective hydro scheduling problem. Here the objectives are to maximize the annual hydropower generation and to satisfy the irrigation requirements as far as possible. A fuzzy-neural model is formulated for operating a reservoir type hydro plant with random inflows. Inflow sequences with specified mean and standard deviation are randomly generated using normal distribution. Two short-term scheduling examples are solved using two-phase neural network