Recent brain imaging studies have provided new insight into how students are able to extend their previous problem solving skills to new but similar problems. It is still unclear, however, what the basis is of individual differences in their success at transfer. In this study, 75 subjects had been trained to solve a set of mathematical problems before they were put into the fMRI scanner, where they were challenged to solve modified versions of familiar problems. A hidden semi-Markov model identified the sequential structure of thought when solving the problems. Analyzing the patterns of brain activity over the sequence of states, we observed that subjects who showed consistent brain patterns performed better. This consistency refers to both how consistently subjects respond to different problems (within-subject consistency), and how brain responses of a given subject deviate from the population average (between-subjects consistency). Early within-subject consistency is particularly predictive of later performance.