Abstract: Based on the neural network and the D-S (Dempster-Shafer) evidential theory, a method was proposed for identifying gas-liquid two-phase flow regimes.Firstly, the differential pressure fluctuation signals were decomposed into 4 levels by the wavelet packet transform.Wavelet packet energy and information entropy of signals in various frequency bands were extracted and two eigenvectors were constructed and then the three statistical parameters and four fractal parameters extracted by the statistical and the fractal theories of the differential pressure fluctuation signals were taken as another eigenvector.Furthermore, the eigenvectors were put into the improved BP neural network and trained to realize the flow regime identification.Taking the preliminary identification as the independent evidence,a final identification was obtained according to the D-S evidential fusion algorithm.Using the air-water two-phase flow regime identification in the horizontal pipe as an example, the implementing process of this method was described in detail.The results showed that the method of multi-characteristic information fusion could achieve a higher identification ability than that of single characteristic.