Abstract: Back-Propagation(BP) neural network was used to simulate runoff and sediment yield of Lou soil and Loess soil on partially covered slope exposed to indoor artificial rainfall events. Output variables including runoff and sediment yield data were obtained for treatments with different combinations of slope, rainfall intensity, and coverage area ratio. Actual rainfall intensity, slope, covered area ratio, runoff starting time, and initial water content consisted of the input variables. Runoff and sediment yield were simulated using the BP neural network and multiple linear regression. The results showed that the average relative errors of the training samples and predicting samples were 18.23% and 5.21%, respectively. The BP neural network model outperformed the multiple linear regression model in terms of fitting accuracy and predictability. Relatively, Lou soil showed higher accuracy than that of Loess soil. The BP neural network has potential in simulating runoff and sediment yield on sloping land, and the simulation capability needs to be verified in the field.