There are a number of ways you can approach this problem (as chl has noted) and you should definitely read the links he gives to other questions.
That being said, here is some advice which you may find useful.
The psych package is quite good for simple analysis of questionnaries.
Download this using install.packages("psych") from a local mirror.
There is a useful pairs.panels() function which will show you the correlations between your variables, their distributions and plot regression lines through the points for you. Its a great graphic, but not to be used if you have over 10 variables.
Your next step should probably be to run a factor analysis.
This can be done with either the factanal function in base R, or with the fa function in psych.
Note that this is likely to produce misleading results if you have a small sample size. You can test how many factors to extract using parallel analysis (fa.parallel in the psych package) or Minimum average partial (VSS in the psych package). This could give you some good ideas of how many factors to retain.
Your question suggests that you have no prior hypotheses about the structure of the instrument which may suggest factoring the questions a number of times and finding the solution that makes the most sense.
You can also assess cronbach's alpha which is calculated as the mean of all possible split half reliabilites. The reason i suggested doing the factor analysis first is that cronbach's alpha tends to give weird results if applied to a questionnaire which has multiple factors. The alpha function in the psych package could be used for this computation.
If you wish to formally test which model is best, then you could look into Confirmatory Factor Analysis, but that might be overkill right now. If you are interested, the sem lavaan and OpenMx packages for R can all carry out this kind of analysis.