Cavitation is a common cause of failure in centrifugal pumps. Because of interaction of several mechanical parts and fluid, the vibration signal of a centrifugal pump is complicated. In this paper, the vibrations of a transparent-casing centrifugal pump are studied. Three states are studied experimentally: no cavitation, limited cavitation and developed cavitation. Each case was also confirmed by visually inspecting the cavitation bubbles. The vibrations of the pump was acquired by using an accelerometer that was attached to the casing. Discrete wavelet transform (DWT) analysis and empirical mode decomposition (EMD) are used to extract classification features from the acquired signals. Using these features, an artificial neural network (ANN) successfully diagnosed the cavitation condition of the pump. Finally, EEMD is also implemented. The results showed the success of EMD and DWT in cavitation diagnosis. The output of EEMD does not show significant change comparing to EMD.