began to devise new filtering with which we can get the information required for each specific topic to easily and efficiently. Operation A filtering system of this style consists of several tools that help people find the most valuable information, so the limited time you can dedicate to read / listen / view, is correctly directed to the most interesting and valuable documents. These filters are also used to organize and structure information in a correct and understandable way, in addition to group messages on the mail addressed. These filters are essential in the results obtained of the search engines on the Internet. The functions of filtering improves every day to get downloading Web documents and more efficient messages. Criterion One of the criteria used in this step is whether the knowledge is harmful or not, whether knowledge allows a better understanding with or without the concept. In this case the task of information filtering to reduce or eliminate the harmful information with knowledge. Learning System A system of learning content consists, in general rules, mainly of three basic stages: First, a system that provides solutions to a defined set of tasks. Subsequently, it undergoes assessment criteria which will measure the performance of the previous stage in relation to solutions of problems. Acquisition module which its output obtained knowledge that are used in the system solver of the first stage. Future Currently the problem is not finding the best way to filter information, but the way that these systems require to learn independently the information needs of users. Not only because they automate the process of filtering but also the construction and adaptation of the filter. Some branches based on it, such as statistics, machine learning, pattern recognition and data mining, are the base for developing information filters that appear and adapt in base to experience. To carry out the learning process, part of the information has to be pre-filtered, which means there are positive and negative examples which we named training data, which can be generated by experts, or via feedback from ordinary users. Error As data is entered, the system includes new rules; if we consider that this data can generalize the training data information, then we have to evaluate the system development and measure the system's ability to correctly predict the categories of new information. This step is simplified by separating the training data in a new