Production activities suffered setbacks due to incessant machine breakdown caused by an incipiet fault that occurred without pprior notification has become worrisome to industrialists or machine users and brought delay to production and products optimization processes. Hence, the need to establish machine signature as a means of assisting plant operators to optimally maintain such machines. Therefore, this paperwork focuses on the establishment of a signature for a developed plantain flour pulverizing machine. In carrying out this research, the pulverizing machine condition data was retrieved under two conditions, namely without degradation; and with imposed degradation at different loading capacities using a data logger to monitor in real-time responses of the machine to operating conditions such as temperature, vibration, and sound. Retrieved data was analyzed using signal analyzer 8.0 in MATLAB software and signal analyzer in Origin software where the signature of pulverizing machine was established under different conditions. A regression model was established for the pulverizing machine using the aforementioned stated conditions. A logarithm function trend describes the operation characteristic note of the machine. Validation of this trend was done, and the result showed that pulverizing machine signature without degradation and imposed degradation have a distinct characteristic. Thus, the study outcome forms the basis for developing a predictive maintenance system for the pulverizing machine which distinguishes it as a novel tool for predictive maintenance and condition monitoring of machinery in the inusstry.