Classification of pitch types outside the laboratory or game environment provide benefits in designing and outlining training routines to reduce injury and improve performance. Current classification approaches are based on vision-based optical data, however these optical data is often not available in training sessions. The aim of the current study is to use machine learning algorithms to classify pitch types, i.e., fastball, change-up and curveball, based on pelvis and trunk IMU data in training sessions. A total of 406 successful pitches thrown by 19 pitchers were used to classify pitch types. Three conditions were tested and evaluated in a binary (fastball versus others) and multi-class (fastball versus change-up versus curveball) classification approach. Condition A included the direct output of the PitchPerfect software, whereas condition B included features normalized by fastball characteristics. Condition C is a combination of both condition A and B. The random forest algorithm demonstrated the best predictions in both the binary and multi-class classification approach based on the highest accuracy and F1 scores, i.e., the harmonic mean between sensitivity and precision. Therefore, the random forest algorithm is the best fit for classifying pitch types based on pelvis and trunk IMU data in training sessions. In addition, the performance of the classification algorithm improved when using a binary classification approach. There were no relevant improvements when using additional features. The random forest algorithm can directly be implemented in the PitchPerfect application. Pitchers can use the pitch type data to track and tune their performances, whereas coaches can use the data to design match and training routines. Future research should focus on larger datasets, i.e., more pitchers and pitches, to pre-classify pitchers with similar pitching characteristics to improve the classification algorithms.