Multivariable processes are found in many industries such as chemical, automobile and aerospace. Most of these processes have time delay. The complex and nonlinear nature of multi input multi output (MIMO) systems makes multivariable control a challenging task. Multivariable control becomes difficult in the presence of loop interactions where different control loops in the system exhibit coupled behavior in the control variables. There are many multivariable control techniques which have been developed to address the above issue, including advanced multivariable control techniques such as model predictive control (MPC). Among them, proportional integral derivative (PID) control has been the most common in industries. Application of fuzzy logic for control problems has been shown to improve the overall performance significantly. Although there are many applications related to SISO based fuzzy PID systems, the application, design and tuning of fuzzy PID systems for multivariable systems are less common. The adaptive and nonlinear nature of fuzzy control allows fuzzy PID systems to handle nonlinear systems more efficiently than using linear PID controllers. The objective of this thesis is to develop a technique to design and tune PID type fuzzy controllers for multivariable process systems. Fuzzy PID tuning is performed using the two level tuning principle, which was only used for SISO processes before. In this method, the overall tuning is decomposed into two tuning levels, low level and high level. The low level tuning is dedicated to devise linear gain parameters in the fuzzy PID system and the high level tuning is dedicated to adjust the fuzzy rule parameters. Six different fuzzy PID configurations are investigated and the necessary formulations to perform the two level control for each configuration are obtained. These six configurations are compared using four process independent criteria and the most effective one is chosen. In the first level, two novel analytical methods for calculating linear gains of fuzzy PID controller are presented. The idea of the first method is to bypass continuous infinite spectrum problem by converting a delay process to a rational discrete model and getting back continuous PID controller from its discrete form which is designed with pole placement. In the second method, using root locus and Nyquist techniques, a dominant pole placement method for designing a controller which can reduce interactions among different loops is presented. Static and dynamic decouplers are used for both methods to decouple the loops of the multivariable systems. The ability of the proposed methods in controlling different processes and rejecting disturbance and measurement noise is shown in Key Words PID Control, Multivariable Control, Two-Level Control, Fuzzy Control, Time Delay System