Today, Inertial Measurement Units is used for control in lower extremity prosthesis studies. In this article, an application related to the analysis and classification of foot movements such as dorsiflexion, plantarflexion, inversion and eversion is presented. This study aims to perform the classification of foot movements to recognize the movement pattern and to adapt to abnormal walking conditions for the robotic foot system. Nine parameters are measured with motion data from the IMU sensor connected to the metatarsal of the foot from eleven volunteers aged 20-34 years. Size is reduced by extracting statistical properties such as sum, mean, standard deviation, covariance, skewness and kurtosis from these parameters. Classification process is performed with classifiers such as Decision Tree, Linear Discriminant Analysis, Naïve Bayes Classifier, K-Nearest Neighbor and Support Vector Machine separately for each person. The classification accuracies obtained for 11 volunteers are averaged and the highest accuracy is obtained with 97.2% for KNN.