Abstract: Historically, model development for advanced process control applications has been a major consideration, demanding significant time and effort. The increased use of advanced control systems in industry creates a need for efficient methods for multivariable system identification that systematically refine process knowledge, leading to models that achieve desirable control performance. Moreover, time-varying changes and the aging of process equipment frequently demand model maintenance and control system tuning during the life of process operation. A comprehensive identification test monitoring procedure can aid in resolving this significant model development challenge. This dissertation presents a plant-friendly identification framework, aimed at developing dynamic models for multivariable systems. The components of the framework include plant-friendly multisine input design, frequency response estimation, control-relevant parameter estimation, and robust loopshaping. These components are implemented in a plant-friendly manner to facilitate industrial implementation. Deterministic, periodic multisine input signals are developed to perform plant-friendly experimental testing. A series of multisine design guidelines are derived based on a priori knowledge to generate a desirable input power spectral density. The use of constrained optimization enforces requirements on manipulated and controlled variables. A control-relevant parameter estimation procedure is formulated for curvefitting frequency responses generated from data into linear Matrix Fractional Description models with Model Predictive Control (MPC)-relevant weightings. The MPC-relevant weights emphasize closed-loop performance requirements in the curvefit. A set of models defined by the curvefitted model and uncertainty bounds are used in a robust loopshaping procedure, based on Structured Singular Value (μ) analysis. Robust stability and performance bounds are computed and used as criteria for defining model adequacy with respect to the end-use control application, and to decide when to halt or continue experimental testing. The framework provides a viable tool for performing experimental testing and controller design of systems involving strong interaction, ill-conditioning, and gain-directionality considerations. The user can conduct identification experimental testing of multivariable systems displaying these challenges while meeting practical plant-friendliness considerations. A series of case studies involving distillation column control are presented to demonstrate the effectiveness of the integrated framework.