How to estimate ALL time-delays across MULTIPLE signals? Imagine there are 100 recordings of the same signal or pattern, but those recordings are not properly aligned with each other in time. In other words, each sample has some unknown time delay in relation to each of its sister samples. The data might look something like this:



The question is: What's the best method for computing the relative time-delays of those signals so that I might align them?

If there were only two signals, computing time-delay would be simple. The best method I'm aware of is to maximize the cross-correlation between the two samples across different delay values.

But what about computing a large set of delays? I'm sure there has to be a robust method for this general task... I just don't know what it's called. Thanks!
There are papers describing a method called multichannel cross-correlation, that recursively estimates time delay of each signal. 

However, If I have not missunderstood you problem you can also select one of your signal to be your reference signal and then repeatedly calculate cross-correlation against that compared to the other signals. that way you can align every signal to your reference point.

EDIT: based on your latest comments:


  So far I've only had a chance to look at the original paper linked (microphones in a sound room). It's very interesting, however it seems to require that you know something about the geometry of the microphones. I had been hoping to find a generalized, system-agnostic method to do multi-channel cross-correlation. Something with which you can just input a set of arbitrary signals or patterns and it outputs the "best fit" delay vector.


What it appears to me that you need then is  blind system identification methods which I can link you some examples. Common methods for this task utilizes cross correlation error minimization but there are some limitations based on your model selection. 

Under-Modelled Blind system identification - cross correlation based  

Blind system Identification for acoustic source localization - Eigenvalue decomposition method

Blind system identification using sparse leaning method - LMS adaptive algorithm based on cross correlation formulation