This study presents a robotic application of neural associative memory-based control system that imparts online learning and predictive control strategies to a cost-effective quadrotor helicopter, the Parrot AR.Drone 2.0. The control system is extended with to tackle a fundamental and challenging problem for the quadcoptor: hovering control. The proposed system is based on self-organizing incremental neural network that includes an associative memory algorithm. The algorithm can cope with a hierarchical data space and complex time-transition dynamics; it enables online incremental learning from manual control, thereby gradually improve the stability against interference such as drift caused by either mechanical impairment or external excitation. In particular, after continuously learning the associative state-command pair of hovering manoeuvre, the system can execute the command associated with current state. The proposed system is evaluated on a realistic AR.Drone quadcoptor to test its capacity to tackle the hovering control problem. The results demonstrate that for the first time, the proposed system effectively offers a novel approach to quadcoptor application of an associative memory-based neural network by successfully tackling a hover task through iterative on-line learning.