Object recognition systems constitute a deeply entrenched and omnipresent component of modern intelligent systems. Research on object recognition algorithms has led to advances in factory and office automation through the creation of optical character recognition systems, assembly-line industrial inspection systems, as well as chip defect identification systems. It has also led to significant advances in medical imaging, defence and biometrics. In this paper we discuss the evolution of computer-based object recognition systems over the last fifty years, and overview the successes and failures of proposed solutions to the problem. We survey the breadth of approaches adopted over the years in attempting to solve the problem, and highlight the important role that active and attentive approaches must play in any solution that bridges the semantic gap in the proposed object representations, while simultaneously leading to efficient learning and inference algorithms. From the earliest systems which dealt with the character recognition problem, to modern visually-guided agents that can purposively search entire rooms for objects, we argue that a common thread of all such systems is their fragility and their inability to generalize as well as the human visual system can. At the same time, however, we demonstrate that the performance of such systems in strictly controlled environments often vastly outperforms the capabilities of the human visual system. We conclude our survey by arguing that the next step in the evolution of object recognition algorithms will require radical and bold steps forward in terms of the object representations, as well as the learning and inference algorithms used. © 2013 Elsevier Inc. All rights reserved.