Method for real-time blind separation and demodulation of digital telecommunication signals, based on observing a sampled version of said signal and including parameters, in particular the equalization coefficients, the phase value (φ), the amplitude of the signal, their frequency and symbol time, said method comprising the following steps: - acquiring, by sampling, a first plurality of the signal, each constituting an input of a network of L processing blocks (G i, F i, H i) also herein referred to as "specialized neurons", each neuron being simulated by the outputs of the preceding block, the first plurality of the signal being submitted as an input to the first block simulating a first neuron of the network in order to generate a plurality of outputs of the first block; each neuron F i being simulated by the outputs of an upstream chain G i and stimulating a downstream chain H i; each set of samples passes through the same processing chain; - the outputs of the last network blocks ideally correspond to the demodulated symbols; - adding a nonlinearity to each of the outputs of the last network block making it possible to calculate an error signal, and propagating said error in the opposite direction of the processing chain ("back-propagation"); - estimating, upon receipt of the error by each neuron (i), a corrective term δ θ i and updating the value of the parameter θ i in each block according to θ i += δθ i; said method characterized in that each neuron (F i) of the network specifically performs: - processing of a "Next" function, implemented and executed in a processing logic sub-block ( F i), to generate outputs from a plurality of observations of the digital telecommunication signal and transmit them to the processing logic sub-block ( H i) of the processing block of the next neuron of the network; this function is generally written as ( X i +1,0, ···, X i +1, m i +1 -1) = next( X i,0,..., X i,mi - 1) where m i represents the number of inputs and m i +1 represents the output number of F i which corresponds to the number of inputs of F i + 1 N; - processing of a "Propagate" function, implemented and executed in a "Propagate" logic sub-block ( F i), to calculate errors ( e i +1,0,..., e i +1, m i +1 -1) provided by the block F i + 1 P or by ( e L - 1,0,···,e L-1,N-1) = ( e 0,··· e N-1) with e j = z J - y J at the end of the chain, namely the input F L − 1 P; for the i-th neuron, this function is generally written as ( e i,0,···, e i, mi-1) = propagate( e i +1,0, ···, e i +1, m i +1 -1) - processing of an "Update" function, implemented and executed in an "Update" logic sub-block ( F i U), to calculate corrective parameters δθ i to be applied to the current value of the internally stored parameter θ i from the errors ( e i +1,0, ···, e i +1, m i +1 -1) returned by the sub-block F i U; this function is generally written as δθ i = update( e i +1,0, ···, e i +1, m i +1 -1). - acquiring, by sampling, a first plurality of the signal, each constituting an input of a network of L processing blocks (G i, F i, H i) also herein referred to as "specialized neurons", each neuron being simulated by the outputs of the preceding block, the first plurality of the signal being submitted as an input to the first block simulating a first neuron of the network in order to generate a plurality of outputs of the first block; each neuron F i being simulated by the outputs of an upstream chain G i and stimulating a downstream chain H i; each set of samples passes through the same processing chain; - the outputs of the last network blocks ideally correspond to the demodulated symbols; - adding a nonlinearity to each of the outputs of the last network block making it possible to calculate an error signal, and propagating said error in the opposite direction of the processing chain ("back-propagation"); - estimating, upon receipt of the error by each neuron (i), a corrective term δ θ i and updating the value of the parameter θ i in each block according to θ i += δθ i; - processing of a "Next" function, implemented and executed in a processing logic sub-block ( F i), to generate outputs from a plurality of observations of the digital telecommunication signal and transmit them to the processing logic sub-block ( H i) of the processing block of the next neuron of the network; this function is generally written as ( X i +1,0, ···, X i +1, m i +1 -1) = next( X i,0,..., X i,mi - 1) where m i represents the number of inputs and m i +1 represents the output number of F i which corresponds to the number of inputs of F i + 1 N; - processing of a "Propagate" function, implemented and executed in a "Propagate" logic sub-block ( F i), to calculate errors ( e i +1,0,..., e i +1, m i +1 -1) provided by the block F i + 1 P or by ( e L - 1,0,···,e L-1,N-1) = ( e 0,··· e N-1) with e j = z J - y J at the end of the chain, namely the input F L − 1 P; for the i-th neuron, this function is generally written as ( e i,0,···, e i, mi-1) = propagate( e i +1,0, ···, e i +1, m i +1 -1) - processing of an "Update" function, implemented and executed in an "Update" logic sub-block ( F i U), to calculate corrective parameters δθ i to be applied to the current value of the internally stored parameter θ i from the errors ( e i +1,0, ···, e i +1, m i +1 -1) returned by the sub-block F i U; this function is generally written as δθ i = update( e i +1,0, ···, e i +1, m i +1 -1).