Protein intrinsic disorder describes the tendency of sequence residues to not fold into a rigid three-dimensional shape by themselves. However, some of these disordered regions can transition from disorder to order when interacting with another molecule in segments known as Molecular Recognition Features (MoRFs). Previous analysis has shown that these MoRF regions are indirectly encoded within the prediction of residue disorder as low-confidence predictions (ie. in a semi-disordered state P(D)xe2x89x880.5). Thus, what has been learned for disorder prediction may be transferable to MoRF prediction. Transferring the internal characterization of protein disorder for the prediction of MoRF residues would allow us to take advantage of the large training set available for disorder prediction, enabling the training of larger analytical models than is currently feasible on the small number of currently-available annotated MoRF proteins. In this paper we propose a new method for MoRF prediction by transfer learning from the SPOT-Disorder2 ensemble models built for disorder prediction.