Abstract This paper presents an orthogonal least-squares (OLS)-based modeling method, named dynamic OLS (D-OLS), for generating recurrent fuzzy models. A dynamic-neuron-based fuzzy neural network is proposed, comprising generalized Takagi–Sugeno–Kang (TSK) fuzzy rules, whose consequent parts consist of dynamic neurons with local output feedback. From an arbitrarily large set of candidate dynamic neurons, the D-OLS method selects automatically the most important ones. Thus, in the resulting model, the consequent part of each fuzzy rule contains dynamic neurons with different time delays. The proposed dynamic model, equipped with the learning algorithm, is applied to two temporal problems, where the effectiveness of the suggested method as well as the advantages of the resulting dynamic model are demonstrated.