This thesis develops a comprehensive methodology for the identification of nonlinear state-space models for complex engineering structures, with a focus on making advanced techniques applicable to real-world systems. The process begins with a non-parametric analysis using the Best Linear Approximation (BLA) to characterize the system's dynamics and nonlinearity type across various excitation levels. This information is then used to identify a robust "underlying linear model" in modal coordinates through a state-of-the-art pipeline combining Polymax and Maximum Likelihood Modal Model (MLMM) estimators. ???? A key contribution of this work is a systematic modal selection approach. By analyzing BLA distortion metrics and sine-sweep data, a small subset of the most nonlinearly-active modes is identified.