This study attempted to forecast Latex Crepe Grade 1 (LC-1) prices of the Sri Lankan market through Seasonal Auto Regressive Integrated Moving Average (SARIMA), Holt Winter’s exponential smoothing (HW_ETS) and Artificial Neural Network (ANN) models. A monthly time series data of LC-1 from January 1986 to December 2020 were used for estimation of LC-1 prices with SARIMA and HW_ETS. For Artificial Neural Network (ANN) models, 70% of the data set was used for training and the rest was used for testing. To forecast LC-1 prices, three time periods viz. three, six and 12 months were used for all models beyond January 2021. The variables, international rubber prices (IntP), exchange rates, Sri Lankan rupee per US$ (SLR_$) and Japanese Yen per US$ (Yen_$) and crude oil prices (Cr_OP) were used with their first two lags as independent variables in ANN, together with 12 lags of monthly LC-1. The forecast performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The selected ANN models (ANN_M1: 12 lags of LC-1 and ANN_M2: 12 lags of LC-1 with Cr_OP and its first 2 lags) yielded relatively better forecast performances than the conventional time series models and the other ANN models tested. For 12 and six months forecasts, model M_1 performed better and for three months forecasts, the model M_2 was found to be the best model with acceptable ranges of MAPE (8-9%) and RMSE (62-63).