A plant for shredding metal waste, comprising: - at least one inlet for metal waste to be shredded; - at least one outlet for shredded metal waste (S'); - at least one working chamber interposed between said at least one inlet and said at least one outlet; - means for shredding metal waste placed in said working chamber; - motor means operatively connected with said shredding means; - sensor means operatively associated to said motor means and/or to said shredding means for monitoring at predetermined time intervals at least one working parameter connected to the wear of said motor means and/or of said shredding means; - at least one data processing unit operatively connected to said sensor means, said at least one data processing unit including: - data storage means for storing for a predetermined period of time the values of said at least one working parameter detected by said sensor means; - an artificial intelligence software program that includes the instructions to carry out the following steps: - calculating, on the basis of said stored values, the standard deviation and the moving average thereof; - processing, on the basis of the standard deviation and the calculated moving average, a trend prediction value of said at least one working parameter in a predetermined prediction time period; - calculating, on the basis of said stored values and said trend value, the damage percentage probability value of said motor means and/or of said shredding means in said predetermined prediction time period; so as to allow a plant manager to plan the maintenance of said motor means and/or of said shredding means during the closure times of the same plant if said damage percentage probability value is above a predetermined threshold value. - at least one inlet for metal waste to be shredded; - at least one outlet for shredded metal waste (S'); - at least one working chamber interposed between said at least one inlet and said at least one outlet; - means for shredding metal waste placed in said working chamber; - motor means operatively connected with said shredding means; - sensor means operatively associated to said motor means and/or to said shredding means for monitoring at predetermined time intervals at least one working parameter connected to the wear of said motor means and/or of said shredding means; - at least one data processing unit operatively connected to said sensor means, said at least one data processing unit including: - data storage means for storing for a predetermined period of time the values of said at least one working parameter detected by said sensor means; - an artificial intelligence software program that includes the instructions to carry out the following steps: - calculating, on the basis of said stored values, the standard deviation and the moving average thereof; - processing, on the basis of the standard deviation and the calculated moving average, a trend prediction value of said at least one working parameter in a predetermined prediction time period; - calculating, on the basis of said stored values and said trend value, the damage percentage probability value of said motor means and/or of said shredding means in said predetermined prediction time period; so as to allow a plant manager to plan the maintenance of said motor means and/or of said shredding means during the closure times of the same plant if said damage percentage probability value is above a predetermined threshold value. - data storage means for storing for a predetermined period of time the values of said at least one working parameter detected by said sensor means; - an artificial intelligence software program that includes the instructions to carry out the following steps: - calculating, on the basis of said stored values, the standard deviation and the moving average thereof; - processing, on the basis of the standard deviation and the calculated moving average, a trend prediction value of said at least one working parameter in a predetermined prediction time period; - calculating, on the basis of said stored values and said trend value, the damage percentage probability value of said motor means and/or of said shredding means in said predetermined prediction time period; so as to allow a plant manager to plan the maintenance of said motor means and/or of said shredding means during the closure times of the same plant if said damage percentage probability value is above a predetermined threshold value. - calculating, on the basis of said stored values, the standard deviation and the moving average thereof; - processing, on the basis of the standard deviation and the calculated moving average, a trend prediction value of said at least one working parameter in a predetermined prediction time period; - calculating, on the basis of said stored values and said trend value, the damage percentage probability value of said motor means and/or of said shredding means in said predetermined prediction time period; so as to allow a plant manager to plan the maintenance of said motor means and/or of said shredding means during the closure times of the same plant if said damage percentage probability value is above a predetermined threshold value.