Assuming roughly identical payouts, about half of the machines will provide "above average" paybacks. If you increase your criteria from "above average" to "top 5%," then 5% or 50 will be in the top 5%; 10 will be the top 1% of paybacks. This isn't particularly insightful for the would-be gambler. Note that even if all the machines are genuinely identical, some will have to perform better than others, but those won't necessarily provide better results for a future game.
Another way to consider this problem is to calculate a mean payout for all 1000 machines over all tens, hundreds, thousands, etc of trials. The overall average based on this very large amount of data is probably pretty reliable. Then you can use a statistical test on each machine's payout and test for a mean response better than the overall mean.
Suppose that there are only two outcomes – you get all your money back plus 0.8 times your money half the time, and nothing the other half. The average payout is 0.5 x 1.8 + 0.5 x 0 = 0.9. Further, suppose that you observed a payout of ratio of 1.26 for a machine with 10 trials (i.e. 7 wins). What is the probability that the machine’s win ratio is really only 0.5 and that this level of winning was mere luck? The probability of winning at least 7 times with a 0.5 win rate is 17% sum(10 choose k, k = 7..10)/2^10, interesting, but hardly statistical proof. A payout ratio of 1.26 over 50 trials (i.e. 35 wins) is only about 0.3% likely, but this is still likely to happen in about 3 of the 1000 different machines. Moving the payout up to 1.44 (80% sample win rate), the odds start to drop off faster. 80% on 50 trials with a machine would only happen with probability 1.2x10^(-5) or 0.0012%, probably indicating a good one to hit. As you can see it takes a good bit of suspect data to be confident in this kind of outlier.
Or you could give up the slots and lose your money in the stock market like a “responsible” adult :)