A suboptimal algorithm to fixed-interval smoothing for nonlinear Markovian switching systems is proposed. It infers a Gaussian mixture approximation to the posterior smoothing pdf by combining the statistics produced by an IMM filter into an original backward recursive process. The complexity is limited, as the number of underlying filters and smoothers is equal to the constant number of hypotheses in the posterior mixture. A comparison, conducted on realistic simulated target tracking case studies, shows that the investigated method performs significantly better than equivalent algorithms.