The quality of an ultrasonic array image depends on accurate information about its acoustic properties. Inaccurate acoustic properties can cause image degradation such as blurring, mislocation of reflectors, and the introduction of artifacts. In this paper, for the specific case of an inhomogeneous and anisotropic austenitic steel weld, Monte Carlo Markov Chain (MCMC) inversion is used to estimate unknown acoustic properties from array data. The approach uses active beacons that transmit ultrasound through the anisotropic weld; the ultrasound is then captured by a receiving array. A forward model of the ultrasonic array data is then optimized with respect to the experimental data using an MCMC inversion. The result of this process is the extraction of a material property map that describes the anisotropy distribution within the weld region. These extracted material properties are then used within an imaging algorithm-the total focusing method in this paper-to produce autofocused images. This MCMC inversion approach is first applied to simulated data to test the convergence, robustness, and accuracy of the method and its implementation. The extracted weld map is used to show improved imaging of defects within the weld, relative to an image formed assuming a constant velocity. Finally, the MCMC inversion approach is used on experimental data from a 110-mm-thick steel plate containing an austenitic weld. Here the extracted weld map is used to show that defect location errors of greater than 5 mm are reduced to around 2 mm when the extracted weld map is used.