demands on computational resources grow very rapidly. The present work proposes a *feed-forward* Artificial Neural Network (ANN) method as a different approach for obtaining the properties of atomic nuclei such as the ground state (gs) energy and the ground state (gs) point proton root-mean-square (rms) radius based on results from readily-solved basis spaces. *Feed-forward* ANNs can be viewed as universal non-linear function approximators [@HORNIK:1989]. Moreover, ANNs can find solution when algorithmic methods are computationally intensive or do not exist. For this reason, ANNs are considered a more powerful modeling method for mapping complex non-linear input-output problems. The output values of ANNs are obtained by simulating the human learning process from the set of learning examples of the input-output association provided to the network. Additional information about ANNs can be found in [@BISHOP:ANN:1995][@HAYKIN:ANN:1999]. Although the gs energy and the gs point proton rms radius are ultimately determined by complicated many-body interactions between the nucleons, the variation of the NCSM calculation results appears to be smooth with respect to the two basis space parameters, $\hbar\Omega$ and $N_{\rm max}$, where $\hbar\Omega$ is the harmonic oscillator (HO) energy and $N_{\rm max}$ is the basis truncation parameter. In practice, these calculations are limited and one 