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This article presents a comparison of artificial neural networks andneuro-fuzzy systems appliedfor modelling andcontrolling a real system. The main objective is to model and control the temperature inside of a kiln for the ceramic industry. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: adaptive neuro fuzzy inference system and feedforward neural networks are described and compared. Finally, real-time control results using internal model control strategy are resented. Using available Matlab software for both algorithms, the objective is to show the implementation steps for modelling and controlling a real system. Finally, the performances of the two solutions were comparedthrough different parameters for a specific real didactic case