Turmeric is the largest herbs and spices potentials of Indonesia. However, the postharvest technology turmeric is still inadequate. Drying is one of the post-harvest processing technologies, to reduce the water content of a food with thermal energy such as sunlight or mechanical equipment. This technology is a new method for predictive modeling of turmeric drying process for on-line monitoring and controlling of this process. It can optimize the drying process to increase value. A back propagation neural network (BPNN) was developed to predict the model of turmeric drying process in a hot air dryer. BPNN inputs were read mean, blue mean, and green mean at time and output was water content at time t + ∆t. The results showed that used BPNN model had better performance than conventional model. The best BPNN graphic model is 0,004 MSE and 25,33% ARE for training set and 0,003 MSE and 20,25% ARE for validation set that built 0.6 of learning process and 0.3 of momentum rate. This model could predict the water content of turmeric at time t + ∆t by knowing the input data at time t. Also, this BPNN model can used for on-line control of the turmeric drying process.