Method for the determination of a laundry weight in a laundry treatment appliance (1, 2, 3) comprising: o Selecting a laundry program in the laundry treatment appliance; o Starting the selected laundry program; o Sensing a plurality of parameters indicating operating conditions of the laundry treatment appliance during the laundry program, wherein said laundry treatment appliance (1, 2, 3) includes a rotatable drum (13, 23, 33) and a motor (14, 24, 35) to rotate said drum, and wherein said plurality of parameters includes one or more parameters indicative of operating conditions of the motor (14, 24, 35) of the laundry treatment appliance (1, 2, 3); characterized in that it also includes the step of: o Predicting a weight of the laundry present within the laundry treatment appliance based on said plurality of parameters by means of a data-driven soft sensor, which includes a step of predicting a weight of the laundry by a supervised learning prediction that predicts an output value from input data, wherein the data driven soft sensor uses a data driven model, wherein the data driven model is obtained by training the data driven model by said supervised learning using a set of training examples, each training example comprising a pair of input data and a respective output value, the input data being values of the operating conditions of the laundry treatment appliance including said one or more parameters indicative of operating conditions of the motor (14, 24, 35) of the laundry treatment appliance (1, 2, 3), and the output value being representative of the weight of the laundry, wherein the input data comprises parameters that change in dependence on the laundry weight in the laundry treatment appliance, and wherein said supervised learning includes predicting the weight of the laundry using a regression algorithm. o Selecting a laundry program in the laundry treatment appliance; o Starting the selected laundry program; o Sensing a plurality of parameters indicating operating conditions of the laundry treatment appliance during the laundry program, wherein said laundry treatment appliance (1, 2, 3) includes a rotatable drum (13, 23, 33) and a motor (14, 24, 35) to rotate said drum, and wherein said plurality of parameters includes one or more parameters indicative of operating conditions of the motor (14, 24, 35) of the laundry treatment appliance (1, 2, 3); o Predicting a weight of the laundry present within the laundry treatment appliance based on said plurality of parameters by means of a data-driven soft sensor, which includes a step of predicting a weight of the laundry by a supervised learning prediction that predicts an output value from input data, wherein the data driven soft sensor uses a data driven model, wherein the data driven model is obtained by training the data driven model by said supervised learning using a set of training examples, each training example comprising a pair of input data and a respective output value, the input data being values of the operating conditions of the laundry treatment appliance including said one or more parameters indicative of operating conditions of the motor (14, 24, 35) of the laundry treatment appliance (1, 2, 3), and the output value being representative of the weight of the laundry, wherein the input data comprises parameters that change in dependence on the laundry weight in the laundry treatment appliance, and wherein said supervised learning includes predicting the weight of the laundry using a regression algorithm.