Solar radiation is the principal and fundamental energy for many physical, chemical and biological processes. However, it is measured at a very limited number of meteorological stations in the world. This paper presented the methods of monthly mean daily solar radiation estimation using support vector machines (SVMs), which is a relatively new machine learning algorithm based on the statistical learning theory. The main objective of this paper was to examine the feasibility of SVMs in estimating monthly solar radiation using air temperatures. Measured long-term monthly air temperatures including maximum and minimum temperatures (T(max) and T(min), respectively) were gathered and analyzed at Chongqing meteorological station, China. Seven combinations of air temperatures, namely, (1) T(max). (2) T(min), (3) T(max) - T(min), (4) T(max) and T(min). (5) T(max). and T(max) - T(min). (6) T(min) and T(max) T(min) and (7) T(max). T(min), and T(max) - T(min), were served as input features for SVM models. Three equations including linear, polynomial, and radial basis function were used as kernel functions. The performances were evaluated using root mean square error (RMSE), relative root mean square error (RRMSE), Nash-Sutcliffe (NSE), and determination coefficient (R(2)). The developed SVM models were also compared with several empirical temperature-based models. Comparison analyses showed that the newly developed SVM model using T(max) and T(min), with polynomial kernel function performed better than other SVM models and empirical methods with highest NSE of 0.999, R(2) of 0.969, lowest RMSE of 0.833 MJ m(-2) and RRMSE of 9.00%. The results showed that the SVM methodology may be a promising alternative to the traditional approaches for predicting solar radiation where the records of air temperatures are available. (c) 2010 Elsevier Ltd. All rights reserved.