Artificial neural networks (ANNs) are well known for their classification abilities. Although choosing hyperparameters such as neuron layer count and size can be a quite tedious task. Pruning approaches assume that a sufficiently large ANN has already been trained and can be simplified with acceptable classification accuracy loss. The current paper presents a node pruning algorithm and gives experimental results for pruned network accuracy rates versus their non-pruned counterparts.