This　paper　describes　how　behavioral　and　imaging　data　can　be　combined　with　a　Hidden　Markov　Model　(HMM)　to　track　participants＇　trajectories　through　a　complex　state　space.　Participants　completed　a　problem-solving　variant　of　a　memory　game　that　involved　625　distinct　states,　24　operators,　and　an　astronomical　number　of　paths　through　the　state　space.　Three　sources　of　information　were　used　for　classification　purposes.　First,　an　Imperfect　Memory　Model　was　used　to　estimate　transition　probabilities　for　the　HMM.　Second,　behavioral　data　provided　information　about　the　timing　of　different　events.　Third,　multivoxel　pattern　analysis　of　the　imaging　data　was　used　to　identify　features　of　the　operators.　By　combining　the　three　sources　of　information,　an　HMM　algorithm　was　able　to　efficiently　identify　the　most　probable　path　that　participants　took　through　the　state　space,　achieving　over　80%　accuracy.　These　results　support　the　approach　as　a　general　methodology　for　tracking　mental　states　that　occur　during　individual　problem-solving　episodes.　(C)　2011　Elsevier　Inc.　All　rights　reserved.