Election into Major League Baseball's (MLB) National Hall of Fame (HOF) often sparks debate among the fans, media, players, managers, and other members in the baseball community. Since the HOF members must be elected by a committee of baseball sportswriters and other entities, the prediction of a player's inclusion in the HOF is not trivial to model. There has been a lack of research in predicting HOF status based on a player's career statistics. Many models that were found in a literature search use linear models, which do not provide robust solutions for classification prediction in complex non-linear datasets. The multitude of possible combinations of career statistics is better suited for a non-linear model, like artificial neural networks (ANN). The objective of this research is to create an ANN model which can be used to predict HOF status for MLB players based on their career offensive and defensive statistics as well as the number of career end of the season awards. This research is limited to investigating players who are not pitchers. Another objective of this report is to give the audience of this particular journal an overview of ANNs.