This paper presents a new neural network architecture based on prior work on Adaptive Resonance Theory (ART) that is capable of incremental learning. Termed IFOSART, it addresses several problems in previous incremental learning ART networks while maintaining their advantages. Based on FOSART, it addresses the plasticity-stability issue by incorporating a concept of time into the network. Also addressed is the problem of handling exceptions in the data, as opposed to simply generalizing the data. IFOSART incorporates a noise removal mechanism to prevent the excessive proliferation of exception categories and to remove any instantiated noise categories from the network. Results of experiments undertaken show that the presented network is comparable in its generalization ability to other types of neural, fuzzy and traditional classifiers while maintaining a fair tolerance towards noise.