This paper presents a neural network based adaptive dead-time compensation strategy for an inverter fed permanent magnet synchronous motor drive. The neural network is used for identifying the dead-time compensation time (DTCT) that includes an equivalent dead-time, turn-on/off time and on-state voltage components of the voltage source inverter. In order to train the neural network, desired DTCTs for eight operating points are prepared as training data. The trained neural network can identify a desired DTCT for any operating point because it has the capability of the interpolation. The accuracy of the identified DTCT is experimentally confirmed by comparing the calculated active power with a measured one.