Models of polymer electrolyte membrane fuel cells (PEMFC) have become increasingly complex, using many parameters to define the behavior of species and the performance of the cell. A framework is presented here to couple an agglomerate electrode based, multi-dimensional, multi-physics mathematical model of membrane electrode assembly (MEA) model with an optimization-based nonlinear least-squares parameter estimation algorithm. The framework is used to estimate the micro-structural parameters of the catalyst layer such as agglomerate size. The results show that a set of data over a range of operating conditions can be accurately described by using a unique set of structural parameters that match experimental visualization of the catalyst layer. Extension of this methodology can be used to systematically estimate any model parameters in order to reduce uncertainty in model predictions. (C) 2012 The Electrochemical Society. [DOI: 10.1149/2.041205jes] All rights reserved.