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Titanium alloys’ properties are sensitive to the microstructure very much, which have nonlinear interactive relationship with the microstructral characteristics. In this study, a model was developed for the prediction of the correlation between microstructure and tensile properties in titanium alloys using artificial neural network (ANN). The inputs of the neural network were quantificational microstructure parameters, including thickness of α-laths, volume fraction of α-laths and Ferret Ratio. The outputs of the model were the tensile properties, including ultimate strength, yield strength, elongation and reduction of area. The model was based on back-error propagation (BP) neural network, and trained with the data collected from isothermal compression experiments of Ti17 alloys. A very good performance of the neural network was achieved such as prediction accuracy and generalization ability. Bayesian regularization and gradient descent learning method can solve the super-fitting problem of high-accuracy training and low-accuracy prediction of traditional BP artificial neural network. The model can be used for prediction of tensile properties of Ti17 alloys according to its microstructural features. Modeling this correlation is fairly necessary to build a robust expert database in titanium expert system.