The control of constrained nonlinear liquid level systems is a problem of fundamental importance in pharmaceutical, chemical, food-processing, oil refining and natural liquid gas separation industries. This paper proposes a novel control strategy for the control of such constrained high-dimensional interacting liquid level systems. The nonlinear liquid level regulation problem is formulated as a path planning problem in high-dimensional state space where constraint satisfaction is viewed as obstacle avoidance. An approximate control policy to steer the system to the goal state while satisfying numerous level and flow-rate constraints is computed using the famous RRT path planning algorithm which can efficiently explore non-convex spaces. To further improve performance a neural network was trained to generalize the approximate control policy computed by the RRT to unexplored states and provide smooth control. The generalized control policy learnt by the neural network is then used to achieve large changes in state and bring the system close to the goal state after which computationally cheap linear control is used to keep the system close to the goal state. The effectiveness of the proposed ANN-RRT control approach is demonstrated by applying it to the control of constrained high dimensional 5, 10, 20, 30 and 50 interacting tank systems. Experimental results for a highly interacting quadruple tank system indicate that the ANN-RRT algorithm significantly outperforms alternate approaches like PID, Fuzzy control, MPC, IMC and SMC from recent literature. © 2020 Elsevier B.V.