Many dynamical systems involve not only process and measurement noise signals but also parameter uncertainty and known input signals. When L2 or H∞n filters that were designed based on a “nominal" model of the system are applied, the presence of parameter uncertainty will not only affect the noise attenuation property of the filter but also introduce a bias proportional to the known input signal, and the latter may be very appreciable. In this paper, we introduce a finite-horizon robust H∞ filtering method that provides a guaranteed H∞ bound for the estimation error in the presence of both parameter uncertainty and a known input signal. This method is developed by using a game-theoretic approach, and the results generalize those obtained for cases without parameter uncertainty or without a known input signal. It is also demonstrated, via an example, that the proposed method provides significantly improved signal estimates.