Diagnostic and monitoring approaches, able to detect and classify the damage and wear process, are becoming of increasing importance. Especially for friction wear-related phenomena which are difficult to measure directly during the operation and their diagnostic has been usually restrained to offline examinations. Permanent contact and repetitive sliding motions between two surfaces lead to material changes. Due to different wear mechanisms, sudden structural changes appear, emitting energy in form of elastic waves known as Acoustic Emission (AE). Therefore, a correlation between the emitted AE and damage level can give important information about the process state and the related knowledge can be used for automated supervision. This contribution introduces an advanced method for wear states identification and classification by means of AE technique and fuzzy-based multi-class classification approach. Compared to the previous publications, here the sequential effect of the motion trajectory is investigated. To establish a relationship between wear mechanism and AE signals, frequency-based feature selection using Continuous Wavelet Transform (CWT) was performed. Five wear process stages were detected during experiments. Results show that the behavior of individual frequency components changes when the wear-related effect changes. Using the CWT transformed signals, statespecific pattern are generated to classify signal features related to specific states. Results show that the introduced method can be used as an online monitoring method for material detection and characterization.