Classification system for microprocessor-assisted identification of obstruction types in sleep apnoea by means of appropriate classification of a snoring-noise signal (Au) to be analysed, comprising: a) an input interface for the respective snoring-noise signal (Au); b) a first classifier adapted to learn in a training mode, when a first plurality of the snoring-noise signals (Au) with a corresponding snoring-noise origin (S1-S4) is input, such that it identifies and outputs, in an identification mode, the most probable type of snoring-noise origin (S1-S4) for the respective snoring-noise signal (Au) from a group of predefined types of snoring-noise origins (S1-S4); characterized by c) a second classifier adapted to learn in a corresponding training mode, when a second plurality of the snoring-noise signals (Au) with a corresponding mouth position (M1-M2) is input, such that it identifies and outputs, in the identification mode, the most probable mouth position (M1-M2) for the respective snoring-noise signal (Au) from a group of predefined mouth positions (M1-M2); d) a third classifier adapted to learn in a training mode, when the snoring-noise origin (S1-S4) identified by the first classifier, the mouth position (M1-M2) identified by the second classifier and an obstruction type from a group of predefined obstruction types are input, such that it identifies, in an identification mode of the respective type of snoring-noise origin (S1-S4) and the mouth position (M1-M2), the input obstruction type as the most probable obstruction type and identifies the most probable obstruction type accordingly from the type of snoring-noise origin (S1-S4) identified by the first classifier and from the mouth position (M1-M2) identified by the second classifier and outputs it in the form of an obstruction type signal, and e) an output interface for the obstruction type signal to a display. a) an input interface for the respective snoring-noise signal (Au); b) a first classifier adapted to learn in a training mode, when a first plurality of the snoring-noise signals (Au) with a corresponding snoring-noise origin (S1-S4) is input, such that it identifies and outputs, in an identification mode, the most probable type of snoring-noise origin (S1-S4) for the respective snoring-noise signal (Au) from a group of predefined types of snoring-noise origins (S1-S4); characterized by c) a second classifier adapted to learn in a corresponding training mode, when a second plurality of the snoring-noise signals (Au) with a corresponding mouth position (M1-M2) is input, such that it identifies and outputs, in the identification mode, the most probable mouth position (M1-M2) for the respective snoring-noise signal (Au) from a group of predefined mouth positions (M1-M2); d) a third classifier adapted to learn in a training mode, when the snoring-noise origin (S1-S4) identified by the first classifier, the mouth position (M1-M2) identified by the second classifier and an obstruction type from a group of predefined obstruction types are input, such that it identifies, in an identification mode of the respective type of snoring-noise origin (S1-S4) and the mouth position (M1-M2), the input obstruction type as the most probable obstruction type and identifies the most probable obstruction type accordingly from the type of snoring-noise origin (S1-S4) identified by the first classifier and from the mouth position (M1-M2) identified by the second classifier and outputs it in the form of an obstruction type signal, and e) an output interface for the obstruction type signal to a display.