How to evaluate clustering success in a completely unsupervised system?
First, PCA is not a clustering method. It is a dimensionality reduction scheme.  You can assess the performance of PCA through analysis of the percent of variance in the dataset that is retained as you decrease the number of dimensions.  Retaining 99%,95%, or 90% is usually ideal, depending on your problem. 

With regards to clustering, you probably want to start with the Silhouette Coefficient.  This combines assessments of both the cohesion (how tight a cluster is) and separation (how well separated each cluster is from other clusters).


Calculate a = average distance of i to the points in its cluster
Calculate b = min (average distance of i to points in another cluster) 
The silhouette coefficient for a point is then given by:
s =1 – a/b, if a &lt; b


It is typically between 0 and 1 with larger numbers being "better".  You can average the coefficients over a cluster or the entire region to get an assessment of the cluster or the entire clustering procedure for your data.

More generally, try googling "assessing clustering" or "cluster validity" to read about all of the other ways that you can score your clustering algorithm.  Here is a very complete treatment of the topic.

Hope this helps!