This thesis presents a critical comparison between three neural architectures for 3D object representation in terms of purpose, computational cost, complexity, conformance and convenience, ease of manipulation and potential uses in the context of virtualized reality. Starting from a pointcloud that embeds the shape of the object to be modeled, a volumetric representation is obtained using a multilayered feedforward neural network or a surface representation using either the self-organizing map or the neural gas network. The representation provided by the neural networks is simple, compact and accurate. The models can be easily transformed in size, position (affine transformations) and shape (deformation). Some potential uses of the presented architectures in the context of virtualized reality are for the modeling of set operations and object morphing, for the detection of objects collision and for object recognition, object motion estimation and segmentation.