Flour extraction rate is a key determinant in milling efficiency and profitability and can be useful in projecting flour output from a mill. Wheat is generally purchased based upon a small group of traditionally measured physical characteristics. This article explores the use of the Single Kernel Characterization System (SKCS) to predict flour extraction rate. Regression analysis was performed using the SKCS parameters and test weight against flour extraction rate for over 600 observations from multiple years from the hard red winter wheat production areas of the U.S. The regression equation had an R 2 of 0.81. The data suggest that the SKCS 4100 and test weight can be used to predict flour extraction rate in hard red winter wheat. Using the regression equation as a tool, mill buyers may be able to make better decisions regarding their wheat purchases and prediction of flour output.