© 2017 The Authors. Published by Eccomas Proceedia. Probabilistic classification requires the computation of the posterior probability distribution of a class given a data observation. In order to generate posterior samples, an analogy has recently been established between the Bayesian updating problem and the engineering reliability problem which allows reliability methods to be applied to the former. The modification of the BUS (Bayesian Updating with Structural Reliability Methods) formulation is based on the conventional rejection principle and suggests the application of Subset Simulation (SuS) from reliability engineering to sample from posterior distributions. Under the original BUS framework a likelihood multiplier is required to be calculated before the implementation of SuS. A recently proposed algorithm learns the likelihood multiplier automatically. This research proposes the utilization of BUS for Gaussian Process classification. The above framework is illustrated using a benchmark Machine Learning dataset from an engineering application.