Building load prediction algorithms are becoming an essential component of building energy technologies as intelligent building technologies are rapidly evolving and require accurate load predictions to make real-time operational decisions. This paper presents a field validation study of an autoregressive with exogenous (ARX) model, indexed with respect to time and temperature, and used for hourly building thermal load prediction with an aim for integration with real time predictive control strategies. Indexing of the ARX model implies that different sets of coefficients are used in the predictive equation depending on different time intervals and temperature ranges. Although many regressive prediction models have been proposed, no field validation has been reported in the literature, which is an essential step before implementation in actual practice. The validation study was carried out using field data from three buildings located in the main campus of Mississippi State University. The proposed model was able to predict hourly thermal load accurately and within the uncertainty bounds of the measured thermal load most of the time. Results also demonstrated that proper indexing of the model allowed it to capture different cooling and heating load profiles and abrupt changes in the load pattern. (C) 2016 Elsevier Ltd. All rights reserved.