There are two strategies that can be employed to solve a problem: analysis and insight. Analysis is the incremental,conscious search for a solution, as in hypothesis testing; insight involves the unconscious restructuring of a problemrepresentation followed by the sudden, conscious realization of the solution (Aha! phenomenon). We attempted to discoverfeatures of neural activity during problem solving that could predict which type of cognitive strategy people used on eachtrial of an anagram task. We used Multivariate Pattern Analysis (MVPA) on 64-channel pre-solution EEG recording thathas been time-frequency transformed. Searchlight was employed in which neighboring time-frequency points within asliding window were used to train a Naive-Bayesian classifier across electrodes to determine the features with the bestclassification accuracy. In addition, Support Vector Machine was trained using principal components, which resulted inimproved classification accuracy than Searchlight, suggesting more distributed nature of informative features in the data.