Traditional bioacoustic classification techniques have been of paramount importance to study and understand biological sound signals. These methods traditionally rest on acoustic feature analysis, including frequency, amplitude, and temporal characteristics that facilitate discrimination among species. Spectrogram analysis, time-domain measurements, and statistical methods are the building blocks of these approaches, enabling insights into species-specific vocalizations and environmental interactions. While bound by traditional feature extraction and computational constraints, conventional methods have seen great success in wildlife monitoring and conservation. They find effective applications ranging from bird song identification to monitoring marine mammals and insect communication studies. These techniques also form the foundation for the assessment of biodiversity and ecosystem health and provide valuable data for ecological research. However, environmental noise, signal overlap, and dataset variability raise several challenges that require their careful implementation. This chapter reviews the principles, methodologies, and real-world applications of bioacoustic classification using traditional techniques while emphasizing their contributions to ecology and conservation. It is only through understanding strengths and weaknesses that the researchers come to appreciate how such techniques remain relevant today and also why they need complementing with modern machine learning and automated approaches. The integration of traditional and advanced approaches will hopefully be able to address the complex bioacoustic challenges in the years to come.