Customer Relationship Management (CRM) is one of the biggest problems for many companies today. By analyzing history records (profiles) of its customers, organization can effectively adapt its business activity to users needs and create better products and services. Proper analysis of customer profiles can help to predict the behaviour of the customers. After grouping customer profiles by similar attributes, company can easier handle its interactions with similar users. Such group profiling can also help to identify needs of new customers on their first interaction with the company. The biggest problem in implementing such systems is the management of a vast array of customer data. Data mining technologies can help to solve this problem and help the ebusinesses to better understand their e-customers. This work reviews data mining methods, such as Nearest Neighbors, Decision Trees and Association Rules, which can be effectively used for customers grouping and profiling. A new conceptual model of Users Recognition System is suggested. The new model uses profiles created from customer history records for identifying new customers. The suggested model has been tested experimentally and results prove the possibility of practical application of this model.