Composite laminates are increasingly being used as primary load bearing members in structures. However, because of the directional dependence of the properties of composite materials, additional failure modes appear that are absent in homogeneous, isotropic materials. Therefore, a stress analysis of a composite laminate is not complete without an accurate representation of the transverse (out-of-plane) stresses.Stress recovery is a common method to estimate the transverse stresses from a plate or shell analysis. This dissertation extends stress recovery to problems in which geometric nonlinearities, in the sense of von Kármán, are important. The current work presents a less complex formulation for the stress recovery procedure for plate geometries, compared with other implementations, and results in a post-processing procedure which can be applied to data from any plate analyses; analytical or numerical methods, resulting in continuous or discretized data.Recovered transverse stress results are presented for a variety of geometrically nonlinear example problems: a semi-infinite plate subjected to quasi-static transverse and shear loading, and a finite plate subjected to both quasi-static and dynamic transverse loading. For all cases, the corresponding results from a fully three-dimensional stress analysis are shown alongside the distributions from the stress recovery procedure. Good agreement is observed between the stresses obtained from each method for the cases considered. Discussion is included regarding the applicability and accuracy of the technique to varying plate geometries and varying degrees of nonlinearity, as well as the viability of the procedure in replacing a three-dimensional analysis in regard to the time required to obtain a solution.The proposed geometrically nonlinear stress recovery procedure results in estimations for transverse stresses which show good correlation to the three-dimensional finite element solutions. The procedure is accurate for quasi-static and dynamic loading cases and proves to be a viable replacement for more computationally expensive analyses.