This paper presents a neural network approach to adaptive control through pattern recognition techniques, extending previously published results on single-input/single-output systems to two-input/two-output systems. Two vector-quantizing neural networks are used to analyze both the input and output patterns resulting from a perturbation to the process. The results of these analyses are then used to update the model gain of the first-order plus dead time model that describes each input/output pair. This work focuses primarily on making model adaptations following load disturbances as opposed to set point changes, as load disturbances present by far the greatest adaptation challenge to chemical process applications. The results are compared to a more traditional modeling technique, batchwise model regression, with respect to both accuracy and computational load. The adaptive strategy is demonstrated using a variety of disturbances on two challenging multivariable process simulations.