The representation of the external world in biological creatures appears to be defined in terms of geometry. In this regard, the author uses the Clifford geometric algebra for the development of geometric type neural networks. The contribution of this paper is the extension of our past work including the use of the SV-Machines in the Clifford algebra framework. Thus, geometric MLPs and RBF networks can be generated using SV-Machines straightforwardly. In this way, we expanded the sphere of applicability of the SV-Machines by the treatment of multi-vectors, which encode the geometry of the data manifold in a rich manner.