Any suggestions for making R code use multiple processors? I have R-scripts for reading large amounts of csv data from different files and then perform machine learning tasks such as svm for classification.

Are there any libraries for making use of multiple cores on the server for R.

or 

What is most suitable way to achieve that?
 A: If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
Parallel Multicore Processing with R (on Windows)
Although the package is not yet on CRAN. it can be downloaded from that link.
A: Shane is correct. Both multicore and Rmpi are winners.
Slightly broader coverage of the topic is in the CRAN Task View on High-Performance Computing.  This also links to a fairly recent survey article on Parallel Computing with R from JSS.  
Lastly, a few hands-on examples and tips are in the Intro to HPC with R tutorial I give once in a while -- see my presentations page for the most recent copy from last week at useR.
A: I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter optimization or cross-validation, still you must write your own functions for that. Or of course you may run the job for different datasets in parallel, if it is a case.
The second issue is memory; if you want to spread calculation over a few physical computers, there is no free lunch and you must copy the data -- here you must consider if it makes sense to predistribute a copy of data across computers to save some communication. On the other hand if you wish to use multiple cores on one computer, than the multicore is especially appropriate because it enables all child processes to access the memory of the parent process, so you can save some time and a lot of memory space.
A: Both Shane and Dirk's responses  are spot on. 
Nevertheless, you might wanna take a look at a commercial version of R, called Revolution R which is built to deal with big datasets and run on multiple cores. This software is free for academics (which might be your case, I dont know)
A: If it's on Linux, then the most straight-forward is multicore.  Beyond that, I suggest having a look at MPI (especially with the snow package).
More generally, have a look at:


*

*The High-Performance Computing view on CRAN.

*"State of the Art in Parallel Computing with R"
Lastly, I recommend using the foreach package to abstract away the parallel backend in your code.  That will make it more useful in the long run.
