The aroma change of aromatic and non-aromatic rice was traced during storage using electronic nose (E-nose). Various steady-state and transient features were derived from the adsorption and desorption phases of the E-nose responses to characterize the ageing process and classify the storage durations. Principal component analysis was utilized to analyse the ageing in terms of seven classes related to the storage duration of 6 months. The aromatic samples followed a specific pathway in its timeline with separately distinctive grouping, revealing the reduction of ageing indices. The aromatic rice underwent crucial changes in its volatile compounds, mainly in the early stages of storage. While, confused grouping of the non-aromatic rice proved its stability due to less diverse range of the aroma compounds. Several artificial neural networks, namely back propagation (BP), radial basis function (RBF), and learning vector quantization (LVQ) were used to classify the storage durations. For the aromatic samples, full classification was achieved by using all the networks. For the non-aromatic rice, the developed RBF and LVQ networks represented full classification by using the features of rise time and polynomial curve fitting parameters. It is concluded that the E-nose system along with the developing methods could be utilized to control the rice ageing.