Efficient algorithms are developed for the model reduction of large scale uncertain systems. The polynomial-time algorithms rely primarily on well-conditioned singular value decomposition computations, and allow robustness stability and performance margins to be computed for uncertain systems of high dimensionality. Application to a realistic description of a large scale paper machine control system demonstrates the utility of the algorithms.