Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2009.4168.4173ShabriA.SamsudinR.IsmailZ.122009923Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. In this study, a hybrid methodology that combines the individual forecasts based on artificial neural network (CANN) approach for modeling rice yields was investigated. The CANN has several advantages compared with conventional Artificial Neural Network (ANN) model, the statistical the autoregressive integrated moving average (ARIMA) and exponential smoothing (EXPS) model in order to get more effective evaluation. To assess the effectiveness of these models, we used 38 years of time series records for rice yield data in Malaysia from 1971 to 2008. Results show that the CANN model appears to perform reasonably well and hence can be applied to real-life prediction and modeling problems.]]>Box, G., G. Jenkins and C. Reinsel,1994Baroutian, S., M.K. Aroua, A.A.A. Raman and N.M.N. Sulaiman,2008Cho, S. and S. Yoon,1997Cho, V.,2003Co, H.C. and R. Boosarawongse,2007De Gooijer, J.G. and R.J. Hyndman,2006He, C. and X. Xu,2005Hill, T., M. O'Connor and W. Remus,1996Hyndman, R.J., A.B. Snyder and S. Grose,2002Lai, K.K., L. Yu, S. Wang and W. Huang,2006Maier, H.R. and G.C. Dandy,2000Ruhaidah, S., S. Puteh and S. Ani,2008Sfetsos, A. and C. Siriopoulos,2004Tang, Z. and P.A. Fishwick,1993Tareghian, R. and S.M. Kashefipour,2007Valenzuela, O., I. Rojas, F. Rojas, F. Pomares and L.J. Herrera et al.,2008Zhang, G.P.,2003Zhang, G., B.E. Patuwo and M.Y. Hu,1998Zou, H.F., G.P. Xia, F.T. Yang and H.Y. Wang,2007Ince, H. and T.B. Trafalis,2006