Hydrates are known to occur in a variety of natural-gas handling facilities and processing equipment in oil fields, refineries, and chemical plants when natural gas and water coexist at elevated pressure and reduced temperature. Prevention of hydrate formation costs large amounts of capital and results in large operating expenses. Hydrate inhibition using chemical inhibitors is still the most widely used method. Accurate prediction of hydrate inhibition is required for cost-effective design and operation. Available models have limitations in ranges of application and types and compositions of the fluids and inhibitors used. This paper describes the development and application of neural networks for the prediction and optimization of natural-gas hydrate inhibition. Neural network models have been used to accurately determine the temperature depression of gas hydrates for a variety of types and concentrations of inhibitors. Experimental data covering wide ranges of hydrate formation conditions, gas compositions, and concentrations of various types of inhibitors have been used in model validation. The factors that may affect the inhibition process, such as gas gravity and pressure, were investigated. An optimization study has been carried out on the selection of inhibitor type and concentration using the developed neural network models. Optimization was based on economical and technical performance considerations concerning inhibitor losses in vapor and liquid hydrocarbons. The results indicate that optimal design depends on water content, operating conditions of pressure and temperature, and gas composition. Optimized hydrate inhibition strategies have been recommended for various gas composition systems.