Using publicly available top-down proteomics (TDP) data sets, we assembled a training set of over 6 million assigned isotope clusters and trained a simple neural network to assign the charge of these clusters. We have incorporated our charge state assignment neural network into a broader workflow to analyze top-down proteomics data. Here, we demonstrate that our neural network approach matches the coverage of existing tools but produces fewer unassigned fragments and greater speed per feature identified.