The method for recognizing the existence of obstacles in a detection zone, particularly in a detection zone adjoining a vehicle, particularly in the vicinity of a vehicle, by means of sensor signals supplied by a plurality of sensors detecting the detection zone, particularly by a plurality of ultrasonic sensors detecting the detection zone, wherein in the method A) for each sensor, by means of a feature extraction from its sensor signal, signal curve characteristics are extracted and a feature vector signal (EF1, EF2, EF3,..., EFm) representing the sensor signal is formed from these features, B) the feature vector signals (EF1, EF2, EF3,..., EFm) are fed to an artificial neural overall obstacle recognition network (ANNO) as input signals, which recognizes at least one obstacle by means of the features of the feature vector signals (EF1, EF2, EF3,..., EFm) and stores information describing this obstacle in a separate obstacle memory EO1, EO2,..., EOp, for each obstacle, C) the information items of each obstacle memory (EO1, EO2,..., EOp) are entered as input data in an artificial neural individual obstacle recognition network (RNN1, RNN2,..., RNNp) dedicated thereto, D) each neural individual obstacle recognition network (RNN1, RNN2,..., RNNp) outputs an obstacle feature vector signal (RO1, RO2,..., ROp) representing the obstacle of the relevant obstacle memory (EO1, EO2,..., EOp) and a number of intermediate feature signals identical to the number of sensors (ROij with i = 1, 2,..., p, with p equal to the number of obstacles, and with j = 1, 2,..., m, with m equal to the number of sensors), each of which is assigned to an individual sensor (S1, S2, S3,..., Sm), E) for each sensor (S1, S2, S3,..., Sm), the intermediate feature vector signals output by the neural individual obstacle recognition networks (RNN1, RNN2,..., RNNp) (ROij with i = 1, 2,..., p, with p equal to the number of obstacles, and with j = 1, 2,..., m, with m equal to the number of sensors) are added to form a corrected feature vector signal (EV1, EV2, EV3,..., EVm), F) for each sensor (S1, S2, S3,..., Sm), a residual feature vector signal (EC1, EC2, EC3,..., ECm) is formed by subtracting the corrected feature vector signal (EV1, EV2, EV3,..., EVm) from the feature vector signal (EF1, EF2, EF3,..., EFm), G) steps B) to F) are repeated using the respectively updated residual feature vector signal (EC1, EC2, EC3,..., ECm), provided that the residual feature vector signal (EC1, EC2, EC3,..., ECm) is greater than a threshold signal, and H) otherwise, the obstacle feature vector signals (RO1, RO2,..., ROp) output by the neural subnetworks (RNN1, RNN2,..., RNNp) each represent an obstacle and, by means of the obstacle feature vector signals (RO1, RO2,..., ROp), potential obstacles located in the detection zone are determined, particularly with regard to position, location and/or orientation and/or with regard to their types, their properties and/or their distances from the vehicle. A) for each sensor, by means of a feature extraction from its sensor signal, signal curve characteristics are extracted and a feature vector signal (EF1, EF2, EF3,..., EFm) representing the sensor signal is formed from these features, B) the feature vector signals (EF1, EF2, EF3,..., EFm) are fed to an artificial neural overall obstacle recognition network (ANNO) as input signals, which recognizes at least one obstacle by means of the features of the feature vector signals (EF1, EF2, EF3,..., EFm) and stores information describing this obstacle in a separate obstacle memory EO1, EO2,..., EOp, for each obstacle, C) the information items of each obstacle memory (EO1, EO2,..., EOp) are entered as input data in an artificial neural individual obstacle recognition network (RNN1, RNN2,..., RNNp) dedicated thereto, D) each neural individual obstacle recognition network (RNN1, RNN2,..., RNNp) outputs an obstacle feature vector signal (RO1, RO2,..., ROp) representing the obstacle of the relevant obstacle memory (EO1, EO2,..., EOp) and a number of intermediate feature signals identical to the number of sensors (ROij with i = 1, 2,..., p, with p equal to the number of obstacles, and with j = 1, 2,..., m, with m equal to the number of sensors), each of which is assigned to an individual sensor (S1, S2, S3,..., Sm), E) for each sensor (S1, S2, S3,..., Sm), the intermediate feature vector signals output by the neural individual obstacle recognition networks (RNN1, RNN2,..., RNNp) (ROij with i = 1, 2,..., p, with p equal to the number of obstacles, and with j = 1, 2,..., m, with m equal to the number of sensors) are added to form a corrected feature vector signal (EV1, EV2, EV3,..., EVm), F) for each sensor (S1, S2, S3,..., Sm), a residual feature vector signal (EC1, EC2, EC3,..., ECm) is formed by subtracting the corrected feature vector signal (EV1, EV2, EV3,..., EVm) from the feature vector signal (EF1, EF2, EF3,..., EFm), G) steps B) to F) are repeated using the respectively updated residual feature vector signal (EC1, EC2, EC3,..., ECm), provided that the residual feature vector signal (EC1, EC2, EC3,..., ECm) is greater than a threshold signal, and H) otherwise, the obstacle feature vector signals (RO1, RO2,..., ROp) output by the neural subnetworks (RNN1, RNN2,..., RNNp) each represent an obstacle and, by means of the obstacle feature vector signals (RO1, RO2,..., ROp), potential obstacles located in the detection zone are determined, particularly with regard to position, location and/or orientation and/or with regard to their types, their properties and/or their distances from the vehicle.