Bayesian smoothing in conditionally linear Gaussian models, also called jump-Markov state-space systems, is an NP-hard problem. As a result, a number of approximate methods - either deterministic or Monte Carlo based- have been developed. In this paper we address the Bayesian smoothing problem in another triplet Markov chain model, in which the switching process R is not necessarily Markovian and the additive noises do not need to be Gaussian.We show that in this model the smoothing posterior mean and covariance matrix can be computed exactly with complexity linear in time