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Televisions and spatial channels have invaded wealthy and poor families. With the growing number of TV channels, the TV viewers become unable to watch what they prefer easily. They will be obliged to zap between thousands of channels permanently to have the right program. With the apparition of DTT (Digital Terrestrial Television), it will be possible for televisions of next generations to receive more than one channel at the same time. Convinced by the importance of the problem, we are working on the concept of the Intelligent Television (AITV). We propose to add to televisions having the ability to receive more than one channel at the same time (this type of television is commercialized and its price will be popular in the next few years) an intelligent layer which can be configured according to the preference of the viewer. If a viewer is watching a program on given a channel and the television detect a program corresponding to his preference, the watched program will be interrupted and it will be switched by the detected program. In that case all viewers will guarantee that no program or subject or scenes will escape them. This may be applied to sports events, to news broadcasts, to documentary ... The idea of this work is to make the television personalized, adapted for each user and able to analyze all the components of the video stream (Auditory analysis, image analysis, and natural language analysis) to filter for every user the suitable programs (see Figure 1). 1) Automatically extracting knowledge from the TV content via combination of text mining, visual semantics analysis, and audio semantics analysis;. 2) Selectively recording all the TV content which may be preferred and desired by consumers. 3) Searching, retrieving and managing the recorded TV content via a simple remote control set. 4) Automatically upgrading itself by deleting the obsolete content and adding new content. (Interactive TV) will also provide new technologies to help the content produc- - ers to ensure that the content produced will reach the right audience, and thus the efficiency and effectiveness of their content production will be significantly improved. To experiment our system, we will use the dataset of TRECVID Workshops (NIST).