The tool life is different when using different tool materials machine Ti6Al4V under different cutting conditions. Based on the experiments of cutting Ti6Al4V using different tool materials, the tool wear prediction model is established with BP neural network theory. The wear prediction model is verified by further experiments. The results show that there is a valid prediction interval when using BP neural network predicts tool wear. When the cutting speed is in the vicinity of the training sample data, the relative error of the tool wear prediction and experimental results is less than 10%, which can meet the actual production. The tool wear prediction model is helpful for cutting tool material selection, cutting speed optimization and predomination the time of changing tool accurately when cutting titanium alloy.