Modern production increasingly relies on the implementation of automated control systems to improve the efficiency, precision, and safety of technological processes. In the agricultural sector, complex technologies involve the processing or disposal of organic waste through biotransformation or degradation. These processes occur in multiple phases, each requiring specific operating conditions. To enhance overall effectiveness, there is a need for an automated system capable of monitoring the biothermal reaction process and managing the operational modes of a biofermenter in accordance with the current phase of organic waste processing. (Research purpose) The aim of this research is to develop an automated control system for a drum-type biofermenter. (Materials and methods) The study was conducted using an experimental drum-type biofermenter operating under conditions of aeration of the processed organic matter. The automated control system is built on a three-level architecture: the upper level consists of a server and an operator’s automated workstation, the middle level includes a programmable logic controller, and the lower level comprises sensors and actuators. Temperature inside the bioreactor is measured using resistance temperature detectors housed in immersion sleeves. Airflow is calculated based on readings from a diff erential pressure sensor. The drum’s rotation speed is monitored using an optical non-contact sensor. (Results and discussion) The proposed control system enables automated monitoring of key processing parameters and supports effective management of the biofermenter’s operating modes. Testing demonstrated the system’s ability to accurately monitor and display the mixture temperature, aeration airfl ow, and drum rotation speed. The system also allows for rapid mode adjustments to operating modes and facilitates the identification of optimal parameters for efficient organic waste processing. (Conclusions) The automated control system for organic waste processing in a drum-type biofermenter ensures continuous monitoring of key parameter. This capability facilitates the identification of optimal operating modes and the development of adjustment algorithms tailored to different types of organic mixtures, ultimately contributing to the production of a high-quality end product.