The Philippine Congress has signed the House Bill 1022 which declares that the Baybáyin script will be considered as the national writing system of the country. The Department of Education, National Commission for Culture and Arts, and some other organizations have vowed to reintroduce this writing system back. This handwriting system is not taught in the schools anymore so an easier training solution is needed. To be able to make this transition easier and faster, a neural network is proposed to convert hand-drawn characters to its corresponding English alphabet syllabication. The type of neural network used in this study is the Long Short-Term Memory (LSTM) network. To gather the necessary number of training images for the neural network, 25 people were employed to draw the characters. All of them used an Android mobile app that will let them draw their own version of a character based on three characters presented to them. All of the contributed handwritten characters are manually viewed to ensure the quality of the sample data. After testing five different LSTM networks, the model with 512 and 256 units in the hidden layers and 128 units in the dense layer is the best model for classifying hand-drawn Baybáyin characters. All of the models are tested up to 50 epochs. Using 8500 images for training and 1200 images for validation, this model has achieved a 95.6% training accuracy and 92.9% validation accuracy.