A novel inversion technique combines rock physics and multiple-point geostatistics. The technique is based on the formulation of the inverse problem as an inference problem and incorporates multiple-point geostatistics and conditional rock physics to characterize previously known geologic information. The proposed implementation combines elements of sampling from conditional probabilities and elements of optimization. The technique provides multiple solutions, all consistent with the expected geology, well-log data, seismic data, and the local rock-physics transformations. A pattern-based algorithm was selected as the multiple-point geostatistics component. Rock-physics principles are incorporated at the beginning of the process, defining the links between reservoir properties (e.g., lithology, saturation) and physical quantities (e.g., compressibility, density), making it possible to predict situations not sampled by log data. Results for seismic lithofacies inversion on a synthetic test and a real data application demonstrate the validity and applicability of the proposed inversion technique.