The article presents the possibilities of using the Asynchronous Delay Tap Sampling and Convolutional Neural Network methods to simultaneously monitor the impairments of Chromatic Dispersion and Optical Signal to Noise Ratio. Using the ADTS method, which allows the presentation of distortions in the form of characteristics, a set of 10,000 images was generated simultaneously disturbed by the combination of CD and OSNR impairments. Next, using the convolutional algorithms of neural networks, the network learning process was carried out (using images obtained from the ADTS method) in order to obtain the best model for recognizing the occurring impairments and predicting their values. After a large number of tests, very good results were obtained ensuring a high adjustment of the models at the level of matching ratio R2 above 0.99 (and even above 0.999 for models for Chromatic Dispersion). Models with such a fit meet the requirements set for monitoring systems to recognize the value of occurring impairments within appropriate accuracy limits
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