Recently, a trajectory model, derived from the hidden Markov model (HMM) by imposing explicit relationships between static and dynamic features, has been proposed. The derived model, named trajectory HMM, can alleviate two limitations of the HMM: constant statistics within a state and conditional independence assumption of state output probabilities. In the present paper, a speaker adaptation algorithm for the trajectory HMM based on feature-space Maximum Likelihood Linear Regression (fMLLR) is derived and evaluated. Results of a simple continuous speech recognition experiment shows that adapting trajectory HMMs using the derived adaptation algorithm improves the speech recognition performance.