A novel procedure for in-silico (virtual) craniofacial reconstruction of human mandibles with multiple fractures from a sequence of Computed Tomography (CT) images is presented. The problem is formulated as one of combinatorial pattern matching and solved in two stages. First, the opposable fracture surfaces are identified using a maximum weight graph matching algorithm where the fracture surfaces are modeled as the vertices of a weighted graph. The edge weights between pairs of vertices are treated as elements of a score matrix, whose values are a linear combination of (a) the Hausdorff distance, and (b) a score function based on fracture surface characteristics. Second, the pairs of opposable fracture surfaces identified in the first stage are actually registered using the Iterative Closest Point (ICP) algorithm enhanced with a graph theoretic improvisation. The correctness of the registration in the second stage is constantly monitored by volumetric matching of the reconstructed mandible with an intact mandible. Experimental results on simulated CT image sequences of broken human mandibles are presented.