In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present "ElectroCom61," a comprehensive multi-class object detection dataset encompassing 61 commonly used electronic components. Our dataset, sourced from the electronic components collection at United International University (UIU) in Dhaka, Bangladesh, comprises 2121 meticulously annotated images. We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. Additionally, we conducted minimal pre-processing to optimise model training and performance. "ElectroCom61" stands as a valuable asset for developing cutting-edge electronic component detection systems, with far-reaching applications in both education and industry. Its potential applications span from interactive educational tools to e-waste management systems and streamlined inventory management processes in electronic manufacturing and automation. The code for technical validation of this dataset is available on GitHub: https://github.com/faiyazabdullah/ElectroCom61.