This paper details an applied investigation of pattern recognition based adaptive control for two-input/two-output systems. Two ART2-A neural networks perform a concurrent analysis of controller error and manipulated input patterns resulting from a set point change or an unmeasured disturbance to the system. This information is then used to adapt the models that describe each input/output relationship. The adaptive strategy is demonstrated on two challenging processes : a pilot-scale continuous distillation column and a simulation of the Shell fundamental control problem. The distillation column demonstrates the applicability of the adaptive strategy to both set point changes and disturbances in a challenging real-world process, while the Shell problem demonstrates the ability of the strategy to handle irregular disturbance dynamics.