If there are no censored observations and all subjects die (experience the event of interest)

If you follow each subject until the event of interest occurs (the event is often death, but survival curves can track time until any one-time event), then the curve will eventually reach 0. At the time (X value) when the last subject experiences the event of interest, the probability of survival will be zero.

If all subjects are followed for exactly the same amount of time

If all subjects are followed for the same amount of time, the situation is easy. If one third of the subjects have yet to experience the event of interest by the end of the study, then the probability of survival is 33%.

If some subjects are censored along the way

If the observations for any subjects are censored, then the bottom point on the survival curve will not equal the fraction of subjects that make it to the end of the study without experiencing the event of interest.

Prior to censoring, a subject contributes to the fractional survival value. Afterward, she or he doesn't affect the calculations. At any given time, the survival probability value is the proportion of subjects followed that long who have survived.

Subjects whose observations are censored - either because they left the study, or because the study ended - can't contribute any information beyond the time of censoring. You don't know whether or not they would have experienced the event of interest after the time of censoring (or do know, but can't use the information because the experimental protocol was no longer being followed). So if any subjects are censored before the last time shown on the survival curve's X axis, the final survival probability shown on the survival graph will not correspond to the actual fraction of the subjects who did not experience the event of interest. That simple survival percentage that you can easily compute by hand is not meaningful, because not all the subjects were not followed for the same amount of time.

When will the survival curve drop to zero?

If the survival curve goes all the way down to 0% survival, that does not mean that every subject in the study experienced the event of interest. Some subjects may have been censored at earlier time points (either because they left the study, or because the study ended before they experienced the event of interest). The survival probability will drop to zero when the observation at the last time point is a subject that experiences the event of interest, and not one that is censored. If your data are sorted by X value (which Prism can do using Edit..Sort), the curve will descend to 0% survival if the last Y value is 1 (event of interest), and will end above 0% if the last Y value is 0 (censored).

In the example below, the event of interest is death. Four of the ten subjects die. But the survival curve descends to zero, not to 60%. Why? Because six subjects were censored between 1 and 27 months. We have no idea what would have happened had they stayed in the study until month 28. Since we don't know if they would have lived or died, their data simply doesn't count after the time of censoring (but definitely counts before that). At time 27, only one subject is still being followed, and she or he died at month 28, dropping the probability of survival down to zero.