In this paper two neural models of basal ganglia function during motor sequential behaviour are presented. Two connectionist models of neuron — like elements that mimic some aspects of anatomy and physiology of cortico — basal ganglia — thalamo - cortical loops have been developed. The aim of this work is to report a new computational model of motor sequence learning guided by reinforcement signals from neuronal systems that evaluate behaviours. The models are partially recurrent neural networks known as Jordan networks trained under a reinforcement learning paradigm. To validate these models, experimental findings of Tanji and Shima [5] on monkeys have been reviewed. The hypothesis that cortico #x2014; basal ganglionic loops learn and perform sequences successfully driven by Reinforcement signals has been demonstrated in computer simulations of the models Presented in this paper.