The analysis of running patterns, especially those associated with fatigue, can help specialists in designing more efficient workouts and preventing injuries in high-performance sports. However, classifying running patterns is not trivial for humans. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this work, ground reaction forces are measured by sensors coupled to the base of a low-cost open-source treadmill. ANNs are used to classify the force signals and to indicate the occurrence of fatigue. Different features, extracted from the force signals, are proposed and investigated. A Genetic Algorithm (GA) is used to select the best features. The experimental results indicate that the ANN is able to classify the running patterns with good accuracy. In addition, some features selected by the GA provide important information regarding the identification of fatigue in treadmill running.