Automated bird species identification is a challenging task in the field of bioacoustics and has gained significant attention in recent years. In this study, we propose a novel approach for bird species identification using audio signal processing techniques combined with a neural network system. The goal is to develop a reliable and efficient automated system that can accurately classify bird species based on their vocalizations. The proposed system consists of two main stages: feature extraction and classification. In the feature extraction stage, we employ various signal processing techniques to extract relevant features from the audio recordings. These features include spectral-based parameters, such as Mel-frequency cepstral coefficients (MFCCs), spectral centroid, and spectral roll-off. Additionally, temporal features, such as zero-crossing rate and energy, are also extracted to capture temporal characteristics of bird vocalizations. The extracted features are then fed into a neural network system for classification. We employ a deep learning architecture, specifically a convolutional neural network (CNN), which has shown great success in various audio classification tasks. The CNN is trained on a large dataset of labelled bird vocalizations to learn discriminative patterns and develop a robust classification model.