Profiling a Number Series

Hi everyone,
I'm new here.
My question is how do you profile a series to determine what kind of distribution it is?
For example a series of returns in the financial stock market.
Is there a way to define the distribution type and characteristics based on a given number series (in this case the returns over time of the stock market)?
Like for example is there a test to determine if it is a normal distribution? Or else what other kinds of distributions? And also how to check its skew and kurtosis. That much I've heard.
Any comments on this? I've a basic understanding of statistics but have no idea about this area.
Thank you!
To check whether your data could be normally distributed, it is best to first make a graph (a histogram or a Q-Q-plot) to see if the distribution of your data is not too skewed (which would be not-normal). The next step is usually to carry out a significance test to see whether your sample distribution significantly differs from a normal distribution. The classical test is "Kolmogorov-Smirnov". This test, as well as alternative tests like Shapiro-Wilk, are available in SPSS and other packages. Kolmogorov-Smirnov can be used to compare your data to other distributions (not normal) as well. For example, SPSS allows you to compare your data to a Poisson distribution.
Nick, thank you for those pointers.
So I believe sophisticated statistics software such as SPSS are able to automatically profile a number series?
Besides a normal distribution, is it able to tell what sort of distribution best fits the data say at a certain confidence interval? I've heard of many types such as the student t-distribution, chi-square, poisson etc.
Also, is it right that a normal distribution could be translated or modified with parameters such as skew and kurtosis such that a data series can still be described as "normal" subject to those changes?
Unfortunately I don't have any sophisticated statistics software at the moment. Just Microsoft Excel. Though I should be able to obtain R which is free.
Thanks in advance!