The application of neural-based methods to optimal satellite subset selection for navigation use is discussed. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigen valuse of its inverse, and thus generates GDOP without inverting a matrix.