A method for self-calibration of the position of a set of sensors of acoustic signals, in particular microphones, arranged in a region of space, comprising: providing in said region of space a set of sources of acoustic events, in particular transducers able to generate acoustic waves; measuring times of flight (t i, j) of said acoustic events between each source of acoustic events and each sensor; and reconstructing the positions (x i, y i, ti) of the set of sensors and the positions (a j, b j, c j) of the sources of acoustic events through a maximum-likelihood estimation procedure executed on the basis of said measured times of flight (t i, j), further comprising the operations of: acquiring times of emission (t E) of said acoustic events; obtaining said times of flight (t i, j) as a function of the respective times of emission (t Ej) among said acquired times of emission (t E); said method being characterized by calculating from said times of flight (t i, j) distances (d i,j) between said sources and said sensors and arranging said distances (d i,j) in a matrix of distances; and calculating a matrix of estimated positions of the sensors and a matrix of estimated positions of the sources of events via a maximum-likelihood procedure comprising performing a nonlinear least-squares optimization that minimizes a cost function between Euclidean distances of the positions of the sensors and of the sources and said calculated distances (d i,j), processing a set of equations associated to said maximum-likelihood procedure for calculating a reduced matrix of the measured distances as a bilinear form that is a function of a reduced matrix of positions of the sensors (X̃) and of a reduced matrix of positions of the events (Ã); executing a procedure of singular-value decomposition of said reduced matrix of the measured distances in order to reduce the number of the unknowns in said maximum-likelihood procedure, in particular from three times the sum of the number of sensors and of the number of acoustic sources to nine. providing in said region of space a set of sources of acoustic events, in particular transducers able to generate acoustic waves; measuring times of flight (t i, j) of said acoustic events between each source of acoustic events and each sensor; and reconstructing the positions (x i, y i, ti) of the set of sensors and the positions (a j, b j, c j) of the sources of acoustic events through a maximum-likelihood estimation procedure executed on the basis of said measured times of flight (t i, j), acquiring times of emission (t E) of said acoustic events; obtaining said times of flight (t i, j) as a function of the respective times of emission (t Ej) among said acquired times of emission (t E); calculating from said times of flight (t i, j) distances (d i,j) between said sources and said sensors and arranging said distances (d i,j) in a matrix of distances; and calculating a matrix of estimated positions of the sensors and a matrix of estimated positions of the sources of events via a maximum-likelihood procedure comprising performing a nonlinear least-squares optimization that minimizes a cost function between Euclidean distances of the positions of the sensors and of the sources and said calculated distances (d i,j), processing a set of equations associated to said maximum-likelihood procedure for calculating a reduced matrix of the measured distances as a bilinear form that is a function of a reduced matrix of positions of the sensors (X̃) and of a reduced matrix of positions of the events (Ã); executing a procedure of singular-value decomposition of said reduced matrix of the measured distances in order to reduce the number of the unknowns in said maximum-likelihood procedure, in particular from three times the sum of the number of sensors and of the number of acoustic sources to nine.