In my humble opinion, it depends on what you want out of the PCA, but that there are two simple plots that are quite common and might be helpful:
To know which variables have high loadings in which principal component, a simple barplot of loadings (as small multiples) will display this pretty clearly.
To look for patterns between samples a scatterplot of scores can sometimes help (e.g. in genetics when you've genotyped a bunch of individuals, a scatterplot of PC1 and PC2 is usually used to look for population patterns).
If you know variable or sample groupings a priori, colour the dots and bars.
ps. I hope it's not bad form to include links, but I've written a small post about these plots and making them in my favourite software. http://martinsbioblogg.wordpress.com/2013/06/26/using-r-two-plots-of-principal-component-analysis/