Google doesn't rank pages with one algorithm. It uses a set of automated ranking systems, each with its own job: understanding what a search means, judging which pages are reliable and original, keeping the results page varied, and demoting spam. Google names its more notable systems in a public guide. Most of them reward the same things: pages that clearly answer the search, are original and trustworthy, and don't break Google's spam policies.
That guide is the most useful primary source on the topic. Much of what's written elsewhere about "the algorithm" is guesswork. This article goes through the systems Google itself lists, explains what each one does in plain English, and shows which ones a business or its agency can actually influence.
What are Google's ranking systems?
Google's guide to its ranking systems says its automated systems look at many factors and signals about hundreds of billions of web pages and other content in its index, and return results in a fraction of a second. Some systems are part of what Google calls its core ranking systems, the underlying technology that produces results. Others handle specific needs, such as crisis information or local news.
Two details from the guide matter for site owners:
- Ranking works at the page level. Google uses signals and systems to understand how to rank individual pages.
- Site-wide signals also count. Google says site-wide signals and classifiers contribute too, but good ones don't mean every page ranks well, and poor ones don't mean every page ranks badly.

Google's How Search Works overview of ranking adds the broader picture. Google's algorithms weigh the words of your query, the relevance and usability of pages, the expertise of sources and your location and settings, and the weight of each factor depends on the kind of search. Google also says it runs hundreds of thousands of experiments every year to improve Search. If you want the basics of crawling and indexing first, our Academy lesson on how search engines crawl, index and rank covers them.
Which systems help Google understand a search?
Several of the listed systems are machine-learning language systems. Their job is to work out what someone means, not just which words they typed:
- BERT helps Google understand how combinations of words express different meanings and intent.
- RankBrain helps Google understand how words relate to concepts, so it can return relevant pages even when they don't contain every word in the search.
- Neural matching matches representations of concepts in queries and pages to each other.
- Passage ranking identifies individual sections, or passages, of a page to judge how relevant the page is to a search.
- MUM can understand and generate language, but Google says it isn't currently used for general ranking. It's used for specific applications, such as COVID-19 vaccine searches and featured snippet callouts.
The practical takeaway is that exact-match keyword stuffing has little to offer. Pages do well when they meet the search intent behind a query and use clear headings, so a relevant passage is easy to find. Our lesson on headings and content structure shows how.








