To improve the performance of three-dimensional object recognition system and reduce computational complexity, a view-based method is proposed. First we extract color moments, texture features and affine invariant moments from the 2D view images of 3D objects. Color moments are robust and insensitive to the size and pose of objects. Texture features can distinguish objects which have similar shapes and different appearance. Affine invariant moments have the invariant properties under affine transformation. These features are combined into a feature vector of 23 elements, and then fed to Support Vector Machine (SVM) for training and recognition. We assessed our method based on two public 3D object dataset: COIL-100 and ALOI. 100% correct rate of recognition was obtained on both dataset when the number of presented training views for each object was 36 (10 degrees interval). When the number of training views was reduced, the correct rate of recognition was also satisfied and outperformed previous algorithms.