Wavelet transforms are an extremely powerful tool when it comes to processing signals that have very "low frequency" components or non-periodic events. Our particular interest here is in the ability of wavelet transforms to remove backgrounds of spectroscopic signals. We will discuss the case of surface-enhanced Raman spectroscopy (SERS) for illustration, but the situation it depicts is widespread throughout a myriad of different types of spectroscopies (IR, NMR, etc.). We outline a purpose-built algorithm that we have developed to perform an iterative wavelet transform. In this algorithm, the effect of the signal peaks above the background is reduced after each iteration until the fit converges close to the real background. Experimental examples of two different SERS applications are given: one involving broad backgrounds (that do not vary much among spectra), and another that involves single molecule SERS (SM-SERS) measurements with narrower (and varying) backgrounds. In both cases, we will show that wavelet transforms can be used to fit the background with a great deal of accuracy, thus providing the framework for automatic background removal of large sets of data (typically obtained in time-series or spatial mappings). A MATLAB((R)) based application that utilizes the iterative algorithm developed here is freely available to download from http://www.victoria.ac.nz/raman/publis/codes/cobra.aspx.