Artificial neural network (ANN) is a practical tool and a powerful alternative to mechanism models in operation of hydrology modeling. In this paper, a three layer back propagation (BP) artificial neural network model was developed to estimate the canopy transpiration of young poplar trees (Populus × euramericana cv. N3016) in Northeast China. The combination of air temperature (T_a), vapor pressure deficit (VPD), solar radiation (R_g) and leaf area index (LAI) was chosen as the input variables, while the transpiration measured by sap flow was chosen as output variable. Observational data in growing season of 2008 and 2010 was used to develop model. The number of neurons in the input layer and output layer was 4 and 1, respectively based on the number of input and output variables. Levenberg-Marquardt (LM) algorithm was selected as the learning algorithm to train the network. Tansig and Logsig function were selected as the transfer function in the hidden layer and output layer, respectively. The learning rate and momentum factor were set as 0.1 and 0.01, respectively. The number of neurons in the hidden layer was optimized as 9 by a trial and error method. So the network structure of the developed model was determined as 4:9:1. After 49 times training, the optimal BP ANN transpiration model was determined. The data samples in 2009 were chosen to evaluate the developed model. Results showed that BP ANN transpiration model can successfully simulate the seasonal variation of transpiration. The slope of the regression equation between the simulated and measured transpiration was 0.99, while R² was 0.85. Maximum and minimum absolute error were 0.28 mm/d and 0.003 mm/d. Mean absolute error and mean absolute relative error were 0.11 mm/d and 9.5%, and Nash-Sutcliffe coefficient of efficiency were 0.83, which all indicated the high accuracy and efficiency of developed BP ANN model. However, compared with the model performance during training process, the accuracy decreased slightly, which turned out the existence of over-fitting. At last, a sensitivity analysis of input variables on transpiration was performed using the connection weights of the developed ANN model to assess the relative importance of input variables. Results showed that the relative contribution of radiation to simulated transpiration (33.46%) was maximal, while that of temperature (16.58%) was minimal. The relative contribution of LAI (30.19%) was larger than that of VPD (19.77%), but less than that of radiation. Magnitude order of correlation coefficient between input variables and transpiration and relative contribution of input variables to transpiration presented the same order of R_g > LAI > VPD > T_a, which provided the physical interpretation of why the developed BP ANN model can well simulate the transpiration despite it did not explain the physical process of transpiration. It must be realized that the data employed for developing ANN model contain important information about the physical process of transpiration. The BP ANN can well learn and remember this kind of information by adjusting its weights during training process, and represent it when new variables in evaluation samples were inputted into the model.