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Most of the researchers have developed Artificial Neural Network (ANN) models to forecast the cutting tool life based on whether it has either a flank wear or a crater wear or a nose wear. In this paper, an attempt has been made to classify the cutting tool wear based on whether it has flank wear, chipped-off cutting edge or a combination of both failures. To obtain the experimental results, both the failures namely flank wear and chipped-off cutting edge have been produced using Electric Discharge Machining (EDM) process. Experiments were carried out using cemented carbide-coated inserts on EN-8 steel and all the responses were acquired by using virtual instruments. The acquired data were analyzed to develop the ANN models to forecast the condition of cutting tool. Vibration and strain data were recorded using accelerometer and strain gauge half-bridge circuit.