Example, with R
Quantiles are values chosen to divide ordered values into predefined portions.
The median (1.1), their 50% quantile, divides these 5 ordered values into 2 equal groups:
If you rank the values in order, the median is their middlemost (= least deviant).
group 1 (<1.1)   group 2 (>1.1)

999999 
0 
1.1 
2 
2.002 
2 values   2 values

50% of 5 values  50% of 5 values

Or you could find their 50% (the p = 0.5 th) quantile with R
Definition and Use
 Quantiles are commonly assumed to divide sets of ordered numbers into equalsized groups.
 Quartiles are expected to divide them into 4 equal groups.
 Deciles are supposed to divide them into 10 equal groups.
 Percentiles should divide them into 100 equalsized groups.
For the 5 numbers listed above, this reasoning may seem of academic interest.
 More practically perhaps, you can regard a set of n different values as n different quantiles.
 The most commonly encountered quantiles are the maximum (the 100% quantile) and minimum (the 0% quantile).
 Since the maximum has none of the values above it, and the minimum has none of them below it, these are called 'extreme' or 'divergent' quantiles.
 Conversely, the range enclosed by the first and third quantiles  termed the interquartile range  can be said to typify the distribution. It is commonly used as a summary statistic of spread. Values lying outside that range may be regarded as unrepresentative or outlying.
Simple formula
Given each item's rank (r) gives the number of items of less than or equal value, this is a usable approximation for large sets of values.
the rank of the pth quantile is pn 
When pn does not correspond to the rank of any value of y you have to interpolate, or choose the best value.
When n is small you may prefer R's default quantile formula (type ?quantile to R for more):
1+p(n1)
Tips and Notes
 Instead of percentages, quantiles are commonly expressed using proportions: Thus the first quartile is the 25% or p = 0.25 th quantile.
 Beware, the simple definitions run into difficulties when some of the numbers have equal values (tied), or where only certain numbers can be observed (discrete variables).
 When applied to sufficiently large sets of (untied) values, the relative rank is virtually indistinguishable from p, the proportion of values below that value.
When applied to small and/or heavilytied sets of numbers, these ways of defining quantiles may differ quite noticeably!
Test yourself
Useful references
 Altman, D.G. & Bland, J.M. (1994) Quartiles, quintiles, centiles and other quantiles. BMJ 309, 996 (15 October).
Full text
 A good introduction to the use of quantiles in medical statistics.
 Wikipedia: Quantile.
Full text
 A bit heavy on formulae and a bit light on explanation.