This will be not so much an answer as a commentary.
The quality depends on several things, including (as Aaron said above) 1) the language pair and 2) the topic, but also 3) the genera and 4) the style of the original, and 5) the amount of parallel text you have to train the MT system.
To set the stage, virtually all MT these days is based off of parallel texts, that is a text in two different languages, with one presumably being a translation of the other (or both being a translation of some third language); and potentially using dictionaries (perhaps assisted by morphological processes) as backoff when the parallel texts don't contain particular words.
Moreover, as others have said, an MT system in no way understands the texts it's translating; it just sees strings of characters, and sequences of words made up of characters, and it looks for similar strings and sequences in texts it's translated before. (Ok, it's slightly more complicated than that, and there have been attempts to get at semantics in computational systems, but for now it's mostly strings.)
1) Languages vary. Some languages have lots of morphology, which means they do things with a single word that other languages do with several words. A simple example would be Spanish 'cantaremos' = English "we will sing". And one language may do things that the other language doesn't even bother with, like the informal/formal (tu/ usted) distinction in Spanish, which English doesn't have an equivalent to. Or one language may do things with morphology that another language does with word order. Or the script that the language uses may not even mark word boundaries (Chinese, and a few others). The more different the two languages, the harder it will be for the MT system to translate between them. The first experiments in statistical MT were done between French and English, which are (believe it or not) very similar languages, particularly in their syntax.
2) Topic: If you have parallel texts in the Bible (which is true for nearly any pair of written languages), and you train your MT system off of those, don't expect it to do well on engineering texts. (Well, the Bible is a relatively small amount of text by the standards of training MT systems anyway, but pretend :-).) The vocabulary of the Bible is very different from that of engineering texts, and so is the frequency of various grammatical constructions. (The grammar is essentially the same, but in English, for example, you get lots more passive voice and more compound nouns in scientific and engineering texts.)
3) Genera: If your parallel text is all declarative (like tractor manuals, say), trying to use the resulting MT system on dialog won't get you good results.
4) Style: Think Hilary vs. Donald; erudite vs. popular. Training on one won't get good results on the other. Likewise training the MT system on adult-level novels and using it on children's books.
5) Language pair: English has lots of texts, and the chances of finding texts in some other language which are parallel to a given English text are much higher than the chances of finding parallel texts in, say, Russian and Igbo. (That said, there may be exceptions, like languages of India.) As a gross generalization, the more such parallel texts you have to train the MT system, the better results.
In sum, language is complicated (which is why I love it--I'm a linguist). So it's no surprise that MT systems don't always work well.
BTW, human translators don't always do so well, either. A decade or two ago, I was getting translations of documents from human translators into English, to be used as training materials for MT systems. Some of the translations were hard to understand, and in some cases where we got translations from two (or more) human translators, it was hard to believe the translators had been reading the same documents.
And finally, there's (almost) never just one correct translation; there are multiple ways of translating a passage, which may be more or less good, depending on what features (grammatical correctness, style, consistency of usage,...) you want. There's no easy measure of "accuracy".