Sean gave a good pointer. There are quite a few other recourses. For example, differences can be measured as lack of similarity. And detecting pattern similarity on a high level was discussed here:


Detecting Kinship from a Pair of Images


Another one on visual similarity is very recent:


Random and Optimal Mathematica Walks on IMDb’s Top Films   

Some "answers" for you. I should have provided a few more details on what I am trying to do.

Luc: you're definitely right.  I'll get a couple of images.  In the mean time - a brief description below will explain the technical problem. 
Sean and Vitaliy - yes I believe some of the things I am looking for will require pattern recognition.  so i will follow up with the links.

PHOTO's:  Imagine that you go outside and take a photo of some cars parked along the street.  The next day you take a similar photo with the same camera (from the same exact location and angle).  Let's suppose also that none of the cars have moved their positions on the street.  However, on the second day you have placed a small object on the hood of one of the cars - like a cell phone or a wallet.  I would like my software to be able to recognize this "different object" by examining the two photographs and comparing them.  However, there are a number of technical problems to solve:

1.  The two images, although identical in pixel counts, may not match up perfectly in the placement of the contents.  The center of the viewing area in one photo might be slightly different than in the other photo - so how do I match up the two photo's based on the actual objects shown in the images?

2.  I can subtract the images - and this is certainly one technique.  But imagine that the photo's have a small amount of noise that affects the pixels.  Subtraction should also pick up this random noise - which means that my subtracted image is covered by a lot of random dots from miscellaneous pixels.  This needs to be taken out somehow.

3.  The BIG problem is that the lighting for the two images may not be identical.  In one case the cars may have been completely in the shade because it was a cloudy day.  In the next photo - the cars may have sunlight falling on some part of them.  So somehow I need to set aside the lighting differences in the image calculations, otherwise subtracting the images will bring out many huge differences due to the lighting changes.

I will make a pair of photo's so this example can be more specific.

kindest regards,
Pete