New developments in molecular biology and materials science are now being applied to designing molecular specificity and recognition into the surface of biomaterials. These novel surfaces are envisioned to have a well-defined array of recognition sites designed to interact specifically with proteins and cells. The development of surface analysis techniques that will provide detailed chemical state information at high spatial resolution is required to investigate the presentation of these recognition sites on a biomaterial surface. Static ToF-SIMS has the potential to provide detailed information about the chemical surface structure of biomaterials with a spatial resolution of 1 micron. However, static ToF-SIMS images containing a full mass spectrum at each pixel can be complex to analyze. The challenge is to develop a method for determining all chemical species present in the surface region along with their location and concentration without a priori knowledge about the sample. Image analysis can be considered to be a 3 step process of denoising, component identification, and image reconstruction. A gold surface patterned with 2 micron lines of ethylene glycol thiol molecules separated by 2 micron lines of fluorinated thiol molecules was used as a model system to evaluate different imaging processing strategies. Effectiveness of various denoising methods (wavelet, boxcar, median, etc.) was evaluated in terms of removing speckle noise, preserving image features, and the time required for denoising. Principal component analysis was used to identify the combination of mass fragments that provided the best image contrast.