A method for weighing a wheeled vehicle, comprising steps of: moving the vehicle onto a weighbridge such that a first set of wheels of the vehicle simultaneously loads input sensors (C1, C2) of the weighbridge, then simultaneously loads output sensors (C3, C4) of the weighbridge; acquiring and sampling, by a processing unit, weighing signals (x i,1 -x i,p) from the input and output sensors, while the vehicle crosses the weighbridge; characterized by the following steps: executing by the processing unit a plurality of iterations (m) of an expectation-maximization algorithm, each iteration comprising the calculation of parameters (p k(m+1), a k(m+1), q k(m+1), Q k(m+1), α k(m+1),j, β k(m+1), e k(m+1)) of a model using a multivariate functional principal component analysis, and the calculation of probability estimates (t i,k(m+1), s i,k(m+1)), the model being based on an approximation of each of the weighing signals by a linear combination of square-integrable basis functions weighted by coefficients (c i), the calculated probability estimates of the model comprising for each weighing signal a probability (t i,k(m+1)) that the weighing signal belongs to a given homogeneous group (k) of weighing signals among several homogeneous groups, and a probability (s i,k(m+1)) that the weighing signal presents an anomaly or not, wherein the probability estimates (t i,k, s i,k) are obtained by considering only eigenfunction coefficients having an impingement greater than a threshold value on the results and by using an operator projecting the eigenfunction coefficients into functional subspaces (SE K), each functional subspace being defined for a respective homogeneous group of the homogeneous groups, the eigenfunction coefficients being obtained from the multivariate functional principal component analysis. moving the vehicle onto a weighbridge such that a first set of wheels of the vehicle simultaneously loads input sensors (C1, C2) of the weighbridge, then simultaneously loads output sensors (C3, C4) of the weighbridge; acquiring and sampling, by a processing unit, weighing signals (x i,1 -x i,p) from the input and output sensors, while the vehicle crosses the weighbridge; characterized by the following steps: executing by the processing unit a plurality of iterations (m) of an expectation-maximization algorithm, each iteration comprising the calculation of parameters (p k(m+1), a k(m+1), q k(m+1), Q k(m+1), α k(m+1),j, β k(m+1), e k(m+1)) of a model using a multivariate functional principal component analysis, and the calculation of probability estimates (t i,k(m+1), s i,k(m+1)), the model being based on an approximation of each of the weighing signals by a linear combination of square-integrable basis functions weighted by coefficients (c i), the calculated probability estimates of the model comprising for each weighing signal a probability (t i,k(m+1)) that the weighing signal belongs to a given homogeneous group (k) of weighing signals among several homogeneous groups, and a probability (s i,k(m+1)) that the weighing signal presents an anomaly or not, wherein the probability estimates (t i,k, s i,k) are obtained by considering only eigenfunction coefficients having an impingement greater than a threshold value on the results and by using an operator projecting the eigenfunction coefficients into functional subspaces (SE K), each functional subspace being defined for a respective homogeneous group of the homogeneous groups, the eigenfunction coefficients being obtained from the multivariate functional principal component analysis. executing by the processing unit a plurality of iterations (m) of an expectation-maximization algorithm, each iteration comprising the calculation of parameters (p k(m+1), a k(m+1), q k(m+1), Q k(m+1), α k(m+1),j, β k(m+1), e k(m+1)) of a model using a multivariate functional principal component analysis, and the calculation of probability estimates (t i,k(m+1), s i,k(m+1)), the model being based on an approximation of each of the weighing signals by a linear combination of square-integrable basis functions weighted by coefficients (c i), the calculated probability estimates of the model comprising for each weighing signal a probability (t i,k(m+1)) that the weighing signal belongs to a given homogeneous group (k) of weighing signals among several homogeneous groups, and a probability (s i,k(m+1)) that the weighing signal presents an anomaly or not, wherein the probability estimates (t i,k, s i,k) are obtained by considering only eigenfunction coefficients having an impingement greater than a threshold value on the results and by using an operator projecting the eigenfunction coefficients into functional subspaces (SE K), each functional subspace being defined for a respective homogeneous group of the homogeneous groups, the eigenfunction coefficients being obtained from the multivariate functional principal component analysis.