Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment, a low-cost eye tracker is employed to record the eye movements during the reading process. A dispersion-based algorithm is implemented to identify fixation from the recorded data. Features pertaining to fixation including duration, path length, landing position and fixation direction are extracted for classification purposes. Five categories of reading pattern have been defined and investigated in this study, namely, speed reading, slow reading, in-depth reading, skim-and-skip, and keyword spotting. We have recruited thirty subjects to participate in our experiment. The participants are instructed to read different articles using specific styles designated by the experimenter in order to assign label to the collected data. Feature selection is achieved by analyzing the predictive results of cross-validation from the training data obtained from all subjects. The average classification accuracies in five random tests are 78.24%, 74.19%, 93.75%, 87.96%, and 96.20% respectively. Further improvements are accomplished by introducing an additional undecided class to address ambiguous reading patterns.