Abstract: Sucker rod is an integral part of the oil well pumping. To reduce the rupture possibility of the rod, it is important to recognize correctly its defects. Discusses the way to recognize the defects by using wavelet transform in combination with neural network. Based on the principle of wavelet package, the signals in time domain detection arc decomposed and enter into every frequency band within which the energy is extracted as an input to BP neural network by the momentum-range method for adaptive learning speed gradient descent, then the output reveals the sucker rod's defects including crack, corrosion pits, partial wear, impairments and indefectible rod. Testing results with lots of data acquired in lab showed that the way to recognize the defects of sucker rod involves both the single and mixed defects.