Cranes occupy a crucial role within the industry. They are used throughout the world in thousands of shipping yards, construction sites, and warehouses. However, payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. Manipulation difficulty is also increased by non-intuitive crane control interfaces. Intuitiveness is characterized by ease of learning, simplicity, and predictability. This thesis addresses the issue of intuitive crane control in two parts: the design of the interface, and the design of the controller. Three novel types of crane control interface are presented. These interfaces allow an operator to drive a crane by moving his or her hand freely in space. These control interfaces are dependent on machine vision and radio-frequency-based technology. The design of the controller based on empirical means is also discussed. Various control architectures were explored. It was concluded that a controller with an input shaper within a Proportional Derivative feedback loop produced the desirable crane response. The design of this controller is complemented with a structured design methodology based on root locus analysis and computer numerical methods. The intuitive crane control systems were implemented on a 10-ton industrial bridge crane; simulation and experimental results are presented for validation purposes.