As ethanol production greatly increased in recent years in the U.S., there has been interest to make the ethanol production process more efficient and economical, therefore maximizing profits. Measuring the amount of ethanol produced from a lot of corn takes days when done by conventional methods. There is a need to develop a rapid method of determining quality of corn for ethanol production. Near-infrared spectroscopy (NIRS) could be useful in this application. This thesis was intended to develop a rapid method using NIRS to predict ethanol production from corn. A partial least squares and a component calculation equation were developed to predict the ethanol yield of corn samples. It was determined that the component calculation was more accurate in validation and more practical for use by ethanol plants. This method uses a component calculation equation including protein, oil, and density values predicted by near-infrared spectroscopy (NIRS) to predict the ethanol yield in gallons per bushel at 15% moisture. Using this method instead of implementing new NIRS calibrations would save time and money involved with new calibrations. The component calculation equation was applied to Iowa corn data from previous crop years as well as a planting date study data. The equation produced expected results when applied to data from previous crop years with increasing ethanol yields from 2005-2008. When applied to the planting date study, the results showed significant losses in grain yield as well as a loss in grain quality for ethanol production at later planting dates. In summary, the component calculation was shown to be able to accurately and rapidly predict ethanol yield of corn samples.