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This dissertation is concerned with the estimation and control of a laboratory heating and ventilation system (Instrutek VVS-400). The system is a 2x2 multi-input multi-output process (MIMO). It has been shown that simple techniques such as the ultimate cycle method do not provide adequate control of the process. The system was interfaced with a PC using Matlab/Simulink via a data acquisition package (Humusoft). Continuous time process identification techniques were applied to the flow and temperature processes. The alternative tangent and point method was used to model the processes, and their interaction, using a first order lag plus delay model. Models were obtained for a range of operating conditions. The accuracy of the flow and temperature measurement transducers were investigated ¾ some inaccuracies were determined. Tests revealed that both processes were continuously non-linear. This pointed toward adaptive control as appropriate. PI/PID controllers were used because both processes displayed a low time delay to time constant ratio. Tuning rules were selected on the basis of minimising the integral of absolute error. A strong interaction effect between the output temperature and input flow rate was reduced considerably using a static decoupler. A gain scheduler was designed, using look-up tables, to continuously interpolate for the most suitable controller settings and decoupler gain, as process operating conditions varied. The design was compared to an average model controller. Validation tests showed that the overall difference in performance was slim. It was concluded that discrete time identification methods would yield more appealing results for the gain scheduler, and that the design could be applied to other MIMO processes with relative ease.