SummaryIn this paper, the parameter identification of bilinear stateâspace model (SSM) in the presence of random outliers and timeâvarying delays is investigated. Under the basis of the observable canonical form of the bilinear model, the system output can be written as a regressive form, and a bilinear state observer is applied to estimate the unknown states. To eliminate the influence of outliers and timeâvarying delays on parameter estimation, we employ the Student's distribution to deal with the measurement noise and use a firstâorder Markov chain to model the delays. In the framework of expectationâmaximization (EM) algorithm, the unknown parameters, delays, noise variance, states and transition probability matrix can be estimated iteratively. A numerical simulation and a continuous stirred tank reactor (CSTR) process demonstrate that the proposed algorithm has good immunity against outliers and timeâvarying delays and offers good estimation accuracy for the bilinear SSM.