Wireless communication systems enable the transfer of data between devices through the transmission of radio signals. These wireless devices often transmit a variety of waveform types called modulation formats. Misidentification of modulation formats between two communication devices could lead to undesirable delays in data transfer and inefficient energy consumption. The role of automatic modulation recognition (AMR) is the identification of the different modulations of transmitted signals. In this project, we explore tree-based AMR methods in the 868 MHz frequency band. An existing work implements a tree-based classifier that is constructed manually by feature inspection. We extend the recent approach by implementing classification tree (CT) and random forest (RF) classifiers, as well as introducing an expanded list of features. Performance is verified via a simulation at different signal to noise ratios (SNR). Signal data is initially preprocessed, and features are extracted and used to train each classifier. Improvements of 14% and 3% success rates are found for RF and CT, respectively using all features and at a SNR of 1 dB.