Results from ultimate analysis, proximate and petrographic analyses of a wide range of Kentucky coal samples were used to predict coal rank parameters (vitrinite maximum reflectance (R(max)) and gross calorific value (GCV)) using multivariable regression and artificial neural network (ANN) methods. Volatile matter, carbon, total sulfur, hydrogen and oxygen were used to predict both R(max) and GCV by regression and ANN. Multivariable regression equations to predict R(max) and GCV showed R(2) = 0.77 and 0.69, respectively. Results from the ANN method with a 2-5-4-2 arrangement that simultaneously predicts GCV and R(max) showed R(2) values of 0.84 and 0.90, respectively, for an independent test data set The artificial neural network method can be appropriately used to predict R(max) and GCV when regression results do not have high accuracy. (C) 2010 Elsevier B.V. All rights reserved.