In this paper we suggest how several competing signal smoothers, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable smoothing algorithm. The proposed heuristic, but statistically well motivated, fusion mechanism allows one to combine practically all kinds of smoothers, from simple local averaging or order statistic filters, to parametric smoothers designed for different hypothetical signal and/or noise models. It also allows one to account for the distribution of measurement noise, and in particular to cope with heavy-tailed disturbances, such as Laplacian noise, and light-tailed disturbances, such as uniform noise. (C) 2010 Elsevier Ltd. All rights reserved.