New computation schemes inspired by biological processes are arising as an alternative to standard von-Neumann architectures, to provide hardware accelerators for information processing based on a neural networks approach. Systems of frequency-locked, coupled oscillators are investigated using the phase difference of the signal as the state variable rather than the voltage or current amplitude. As previously shown, these oscillating neural networks can efficiently solve complex and unstructured tasks such as image recognition. We have built nanometer scale relaxation oscillators based on the insulator–metal transition of VO2. Coupling these oscillators with an array of tunable resistors offers the perspective of realizing compact oscillator networks. In this work we show experimental coupling of two oscillators. The phase of the two oscillators could be reversibly altered between in-phase and out-of-phase oscillation upon changing the value of the coupling resistor, i.e. by tuning the coupling strength. The impact of the variability of the devices on the coupling performances are investigated across two generations of devices.