The objective of this paper is to explore the use of wavelet transform methods in the characterization and identification of intensity and type of localized corrosion using electrochemical noise (in this case, fluctuations in potential). A series of experiments were conducted using two identical stainless steel electrodes inserted into known corrosive media. The fluctuations in corrosion potential (electrochemical noise) generated from these electrodes were monitored periodically (about every hour) over a few days. These fluctuations were stored in blocks of 1024 data points (collected at 1 Hz). They were analyzed by using conventional signal processing techniques in the time and frequency domains as well as by using wavelet techniques in the time-frequency phase plane. The results were compared with microscopic visual examination of the corroded surface to correlate the electrochemical noise signal with the type and extent of corrosion. The results show that wavelet transforms hold promise in decoding this noise pattern. A method to compute the corrosion intensity from the phase plane data is proposed and is in qualitative agreement with the experimental results.