A computer implemented method for the diagnosis of anomalies in a refrigeration system, wherein said refrigeration system comprises at least a refrigeration chamber, an evaporator, a compressor and a condenser, the method comprising: - a learning mode process comprising performing measurements of several operating parameters of the refrigeration system when operating under known working conditions with no anomalies; and - an evaluation process comprising performing measurements of at least said operating parameters when the working conditions of the refrigeration system are unknown, and evaluating the values obtained from said measurements; wherein: - said learning mode process further comprises defining a plurality of different anomalies in the operation of the refrigeration system, each of said anomalies associated to corresponding one or more specific anomalous variations of one or more of said operating parameters; and - said evaluation process comprises automatically diagnosing that one of said different anomalies has occurred when the specific one or more anomalous variations associated thereto have been detected; characterized in that said different anomalies include the following anomalies associated to the following indicated anomalous variation of operating parameters: - Dirtiness in the condenser, associated to an increase in both, the condensing pressure and the degree of subcooling; - Dirtiness in the evaporator, associated to a decrease in the evaporating pressure; - Dirtiness in the evaporator and in the condenser, associated to an increase in the condensing pressure, to a decrease in the evaporating pressure, and to an increase in the degree of subcooling; - Leaks of the refrigerant and dirtiness in the condenser, associated to an increase in the condensing pressure, and to a decrease in the degree of subcooling; - Leaks of the refrigerant and dirtiness in the evaporator, associated to a decrease in all of the evaporating pressure, condenser pressure and the degree of subcooling; and - Leaks of the refrigerant and dirtiness in the evaporator and in the condenser, associated to a decrease in the evaporating pressure, an increase in the condenser pressure, and a decrease in the degree of subcooling. - a learning mode process comprising performing measurements of several operating parameters of the refrigeration system when operating under known working conditions with no anomalies; and - an evaluation process comprising performing measurements of at least said operating parameters when the working conditions of the refrigeration system are unknown, and evaluating the values obtained from said measurements; - said learning mode process further comprises defining a plurality of different anomalies in the operation of the refrigeration system, each of said anomalies associated to corresponding one or more specific anomalous variations of one or more of said operating parameters; and - said evaluation process comprises automatically diagnosing that one of said different anomalies has occurred when the specific one or more anomalous variations associated thereto have been detected; - Dirtiness in the condenser, associated to an increase in both, the condensing pressure and the degree of subcooling; - Dirtiness in the evaporator, associated to a decrease in the evaporating pressure; - Dirtiness in the evaporator and in the condenser, associated to an increase in the condensing pressure, to a decrease in the evaporating pressure, and to an increase in the degree of subcooling; - Leaks of the refrigerant and dirtiness in the condenser, associated to an increase in the condensing pressure, and to a decrease in the degree of subcooling; - Leaks of the refrigerant and dirtiness in the evaporator, associated to a decrease in all of the evaporating pressure, condenser pressure and the degree of subcooling; and - Leaks of the refrigerant and dirtiness in the evaporator and in the condenser, associated to a decrease in the evaporating pressure, an increase in the condenser pressure, and a decrease in the degree of subcooling.