Based on a BP neural network (NN) model, we investigated the possibility to derive the tide level at one site using the tide observation at a nearby site. As an example, the application of the neural network model to predict the tide level at Station Sanya using the observed data at Station Xisha is presented. The observed tide level at different hours ahead of the prediction time (t-N+1, …, t-1, t) are used as the input vector. Combinations of different numbers of input-layer nodes (2 to 10) and hidden-layer nodes (3, 4, 5, 10, 15) are used, and the outputs are compared with field data. For the specific case used in this study, four nodes in the hidden layer lead to the best prediction while 15 nodes performs the worst; two nodes in the input vector are the most suitable while more input nodes lead to degraded performance. The model setup with a hidden layer of four nodes and an input vector of two variables (t-1, t) has the best prediction accuracy in this case, with a correlation coefficient of 0.9901, a root-mean-square (RMS) error of 0.06 m, and a prediction error of -0.16~0.15 m (between prediction and observation). The proposed NN model is examined for its applicable to predict tides at a station using the observed tide data of a nearby station if they are physically coherent with similar tidal type.