In many real-world environments, data samples arrive as continuous streams, and they often contain noise. In this study, we aim to undertake noisy data classification in an online learning setting using a hybrid supervised Adaptive Resonance Theory (ART) neural network model. Specifically, Fuzzy ARTMAP (FAM) is a neural network that is capable of learning new categories incrementally by creating or updating prototype patterns whenever an input is similar enough, subject to a threshold to decide when to form a new category. In view of FAM’s ability in tackling the stability-plasticity dilemma, it is utilised as a baseline model for developing a robust classifier with online learning capability for noisy data classification. We equip FAM with pre-processing and post-processing modules to improve its robustness in combating noise in data samples and enhance its online classification performance. On the one hand, the pre-processing feature extraction module exploits the capability of a feedforward neural network to enhance feature representation capabilities. On the other hand, the post-processing module leverages an ensemble structure with majority voting for minimizing classification errors in the presence of noisy data. Evaluated on benchmark noisy datasets, the hybrid FAM model outperforms the base FAM and other variants, offering a robust online learning model for solving noisy classification problems in data streaming environments. The source code can be obtained from https://github.com/Michael-cyber123/eNeuralFAM .