Ladar range image is closely relative to the physical structure property of object surface, and reflects essential characteristics of objects, thus it has received considerable attention in object recognition field. Image moments are significant methods for object recognition. The low order moments of Hu moment invariants and affine moment invariants are combined to describe the features of three-dimensional objects, and back-propagation (BP) neural network is applied to recognize objects on different azimuth angles. When the view angle is constant, the case that training 10 objects and sampling from 3 to 19 per object, under different carrier-to-noise rates (CNRs), is analyzed about the correct rate of recognition for the Hu moment invariants, the affine moment invariants and the combination of both. The theoretical and experimental analyses prove that using the combination of the Hu moment invariants and the affine moment invariants as features to classify objects can achieve better recognition performance than only using either the Hu moment invariants or the affine moment invariants.