Successful MFP requires a knowledge of the bathymetry and the existence of a suitable geoacoustic model for the sound-speed profile and the bottom characteristics. In practice, these parameters are known only approximately, and it is desirable to obtain more accurate estimates to improve the effectiveness of MFP. This aim may be achieved through matched-field inversion (MFI) of data obtained using a source at a series of known locations in the environment. Development of effective inversion procedures requires the use of appropriate numerical optimization techniques suited to the nature of the multimodal multidimensional matching functional to be optimized. This study examines several approaches to global optimization, including simulated annealing, a genetic algorithm, and a combined random search/local optimization approach. Results comparing these approaches were obtained using a model multimodal function with provision for correlated parameters; the performance of several local optimization algorithms was also compared using this model function. For the case of a realistic range-independent waveguide, the functional interactions between several sensitive model parameters were examined by generating 2-D plots of the matching function as a function of selected parameter pairs, using the normal mode program ORCA to generate the fields for matching. Results of inversion for range-dependent environments using this system will be presented and discussed.