A recognition methods for storage time of tea was set up based on the Huangshanmaofeng tea under storage time of 60, 120, 180, 240, 300 and 360 d detected by electronic nose. According to response curves of electronic nose, a set of essential characteristic variables were selected. On the basis of these variables, principle component regression(PCR), partial least squares regression(PLS) and back propagation neural network(BPNN) was applied to build the prediction model for storage time of tea, respectively. Three prediction models were validated by test sample set. The results indicated that standard error of prediction of PCR, PLS and BPNN models were 10.05, 6.04 and 3.21 d, respectively; the maximum relative error 11.03%, 7.02% and 5.89%, respectively; the mean relative error 6.73%, 4.74%, and 3.62%, respectively; determination coefficient between predicted value and real value 0.862, 0.896 and 0.987, respectively. All of the models could predict storage time of tea well. BPNN was the model with the best performance and PLS is better than PCR.