A new approach based on phase features combined with neural network model is proposed for recognizing 3-D objects. The phase features of an object were extracted by wavelength-scanning digital holography and numerical reconstruction technique. A BP neural network with one hidden-layer trained by reconstructed images of three pyramids was used to recognize other pyramids with some variance, and the correct recognition rate of these pyramids is up to 100%. The simulation results demonstrate that the method is effective.