Selecting the appropriate type of image sensor is essential for reliable and accurate performance of vision applications. In this talk, we explore some of the critical factors to consider in selecting an image sensor, including shutter type, dynamic range, resolution and chromaticity. We explore the impact of these factors through three distinct use cases: defect detection, license plate recognition and crowd counting. For defect detection, we examine the advantages and disadvantages of monochrome image sensors. In license plate recognition, we highlight the importance of global shutter, pixel size, dynamic range and color space, providing a comprehensive introduction to key factors that contribute to successful recognition in varying light conditions. Lastly, we explore the trade-offs of high-resolution cameras in crowd counting applications and offer practical insights on developing fast and accurate machine learning models with high-resolution input.