The fused deposition modeling (FDM) process, also known as 3D printing, deals with the manufacture of parts by adding layers of fused filament. Research on manufacturing process monitoring is on the rise, with an emphasis on investigating low-cost transducers as substitutes for the traditional, pricier options. The present study addresses a critical gap in the literature concerning the monitoring of the FDM process using acoustic signals from an electret microphone attached to the extruder. By employing an extensive signal processing and feature extraction analysis, including RMS values, ratio of power (ROP), and count statistics, this research uncovers distinguishable patterns in raw signals that relate to different machine conditions such as normal operation, extruder clogging, and filament shortages. Additionally, machine learning algorithms, specifically neural networks and support vector machine (SVM), are utilized to classify these machine conditions. Notably, signal filtering is found to significantly improve the classification models. The spectral analysis further contributes to characterizing the printing process, especially in identifying frequency values associated with defects. In conclusion, the methodology developed in this study holds promise for real-time monitoring systems, as it showcases high accuracy in classifying machine conditions and offers the potential to ensure quality and detect anomalies early in the printing process. Future research is encouraged to refine the methodology and explore its scalability across different FDM systems and materials.