It really depends on the scene - that is, the set of image bands you are using for your area of interest. For starters, does your scene span multiple tiles, or is it contained inside one? If it spans multiple tiles you might want to engage in color balancing of some sort. You'll also want to check the cloud coverage for each tile, for each year. Also, I would ensure that all of your tiles were collected around the same date (such as a summer month, five years apart), and that the date is sensible - winter in the Chesapeake will show a lot of snow and ice blending with water in classification.
You'll also want to look at atmospheric distortion, and possibly other effects that might negatively impact a classification algorithm (striping is a sensor issue that occasionally affects Landsat, but not usually). You can check these effects by running a spectral correlation tool such as PCA (principal component analysis) on your band files, and examining the results. Spectral correlation should be high in quality data.
You can also simply view each year's imagery as a color composite - Natural Color and IR combinations will quickly show you whether the imagery is clear or not, or if there are obvious issues like atmospheric distortion. From there, a Stretch function run on each band for each year's data might significantly enhance the results of your classification. View histograms for each band and Stretch the bands to isolate the ranges of reflectance values present - this will magnify the spectral properties prior to classification.
The success of a classification will also depend highly on your training samples and input vector data, if you are using an Isocluster or Max Likelihood algorithm. Landsat imagery is quite coarse in resolution, and pixels often contain features across multiple classes. Final thought might be, examine the purpose of your classification. If you are trying to map urban, agricultural, water and beach, Landsat should be fine, but anything more specific than that and you might want to examine Hyperspectral options such as NASA's AVIRIS imagery.
Before and after running a stretch function - blue is natural color as it was downloaded, the second image has a stretch function run on each band, 4.2 standard deviations n from the statistical mean for values in that band.