It seems that a number of the statistical packages that I use wrap these two concepts together. However, I'm wondering if there are different assumptions or data 'formalities' that must be true to use one over the other. A real example would be incredibly useful. 
Principal component analysis involves extracting linear composites of observed variables.

Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.

In psychology these two techniques are often applied in the construction of multi-scale tests
 to determine which items load on which scales.
They typically yield similar substantive conclusions (for a discussion see Comrey (1988) Factor-Analytic Methods of Scale Development in Personality and Clinical Psychology).
This helps to explain why some statistics packages seem to bundle them together.
I have also seen situations where "principal component analysis" is incorrectly labelled "factor analysis".

In terms of a simple rule of thumb, I'd suggest that you:


Run factor analysis if you  assume or wish to test a theoretical model of latent factors causing observed variables.
Run principal component analysis If you want to simply reduce your correlated observed variables to a smaller set of important independent composite variables.

