We investigate experimentally a novel model-free control strategy, called Machine Learning Control (MLC), for aerodynamic drag reduction of a 3D bluff body. Fluidic actuation is applied at the blunt trailing edge of the body combined with a curved deflection surface. The impact of actuation on the flow is monitored with base pressure sensors. The applied model-free control strategy detects and exploits nonlinear actuation mechanisms in an unsupervised manner with the aim of minimizing the drag. Key enabler is linear genetic programming as simple and efficient framework for systems with multiple inputs (actuators) and multiple outputs (sensors). The ansatz of control laws include periodic forcing, multi-frequency forcing and sensor-based feedback control. Approximately 33% base pressure recovery is achieved by the optimal control law for a turbulent flow at Re_H ≈ 3 × 10 5 based on body height.