Hydro power plants are one of the most cost-effective renewable technologies which have the potential to replace the conventional fuels. However, reliability of the plant varies with time due to irregularity in flow pattern of the river which depends on the pattern of climatic parameters like precipitation and evapo-transpiration. The reliability of the plant performance also varies with the efficiency of the power equipment and conveyance coefficient of penstock, both of which are time dependent. When reliability reduces, performance efficiency also gets degraded. That is why, monitoring of reliability in hydro power plants is essential and required to be conducted periodically. But at present, such activities are expensive due to the requirement of extensive surveys, data collection and performance audits of the equipment and structures involved in the power production. There is a lack of single indicator based real time monitoring processes which can be utilized to continuously regulate the reliability of the power plant. Such system can reduce both cost and time required for conducting reliability assessment procedures. That is why, in the present study, an objective and cognitive technique was utilized to develop a single index for real time monitoring of the reliability of a power plant. The results indicate that efficiency of generator, levelized cost of electricity and rate of selling per unit electricity are the most significant indicator which have the capability to represent reliability. A model developed for the present study by Polynomial Neural Network also ensures that regular monitoring of reliability of an HPP is possible.