Intricacies of computer vision are explored, focusing on object recognition and classification. This chapter explores the fundamental principles, methodologies, and applications of these critical aspects in computer vision. Starting with an introduction to the field's transformative impact across various domains, we discuss the convergence of computer science, artificial intelligence, and signal processing that underpins computer vision. The chapter covers the essential stages of object recognition and classification, including image acquisition and filtering, feature extraction, feature selection, model training, and evaluation. Traditional techniques and modern deep learning approaches, particularly convolutional neural networks (CNNs), are examined in detail. The advantages of deep learning, such as automated feature extraction and superior accuracy, are highlighted alongside traditional methods. The importance of privacy and security in the proliferation of computer vision technologies are addressed, emphasizing the need for robust measures and ethical considerations. The application scope of these technologies is vast, ranging from autonomous systems and medical imaging to security and entertainment. Future directions in the field are discussed, focusing on the potential for more real-time applications, advancements in autonomous systems, and the increasing importance of personal data privacy. Overall, this chapter provides a comprehensive overview of the current state and future potential of object recognition and classification in computer vision, underscoring its significance in enhancing human capabilities and transforming industries.