A novel inferential strategy for controlling end-product quality properties by adjusting the complete trajectories of the manipulated variables is presented. Control through complete trajectory manipulation using empirical models is possible by controlling the process in the reduce space (scores) of a latent variable model rather than in the real space of the manipulated variables. Model inversion and trajectory reconstruction is achieved by exploiting the correlation structure in the manipulated variable trajectories captured by a partial least squares model. The approach is illustrated with a condensation polymerisation example for the production of nylon and with data gathered from an industrial emulsion polymerisation process. The data requirements for building the model are shown to be modest.