Assembled structures are nonlinear. The sources of this nonlinearity could include the jointed interfaces, damage and wear, non-idealized boundary conditions, or other features inherent in real parts. To study these systems and to ascertain if they will be operating in a regime in which the nonlinearity is prominent,ﾠnonlinear system identificationﾠtechniques are needed to assess and characterize the nature of the nonlinearity in the structure. Significant progress over the last few years has focused on using nonlinear system identification to identify damage and other deviations from idealized structures. This research reviews nine different methods for nonlinear system identification (restoring force surface,ﾠHilbert transform, directﾠquadrature, zero-crossing,ﾠshort-time Fourier transform, Gaborﾠwavelet, Morlet wavelet, Morse wavelet, and a neural network-based algorithm) in order to assess their accuracy. The methods are compared by identifying characteristics of two systems: a singleﾠdegree of freedomﾠmodel of a Duffing oscillator and measured data from a jointed structure. Asﾠneural networksﾠare not commonly used for system identification, multiple variations of the method are investigated to study its effectiveness.ﾠPerturbationﾠanalysis is conducted to see the efficacy of the different methods for identifying parameters across a large range of design spaces, and the advantages and disadvantages of each method are discussed. The primary contribution of this paper is a comparison on both analytical and experimental data of multiple widely used system identification methods, and an assessment of when each method is most and least applicable, specifically in the context of jointed structures.