In the current deregulated competitive electricity market and with tighter environmental regulations, it is an important goal that power plant boilers are operated and controlled in the most efficient, economic and cleanest manner while fulfilling the grid load demand requirements. This paper presents the development and testing results of a boiler performance analysis and optimization tool referred to as “Smartsheet”, capable of assisting plant owners and operators in meeting the stated optimization goals. The Excel based tool includes a neural network process model, economic relationships, an optimization solver and a number of user functions and interface for use in the analysis and the operational optimization of a circulating fluidized bed (CFB) boiler. In view of the fact that a CFB boiler process is nonlinear with strong interactions among process variables, an artificial neural network (ANN) based model was developed to capture the nonlinear relationships between the variables representing operating conditions and the variables that relate to operating costs. The tool has been tested at the AES Thames, station in Uncasville Connecticut. The validation testing involved developing two orthogonal test matrices based on Design of Experiments (DoE) methods that were then used to carry out two independent tests in the Alstom supplied CFB boiler (100MW). The test data collected from the first set of tests was used to train the ANN model and the data from a second set of tests was used for the model validation. The model was combined with the other software developed functions into a tool to support power plant engineers and operators in boiler and total plant performance analysis. The tool is specifically designed to assist in generating economically optimal operating plant settings based on a utility’s specified current cost and emission credit factors. The optimization results to date show that the optimized operating settings can save, on average, more than 2% of operating costs over the current operating conditions, which have been fine tuned by almost 20 years of operating experience. Additional validation tests of the optimal operating conditions suggested by the optimization tool have been planned with the customer to further validate the tool.