Objective classification of daily weather is performed for 9 vs. 5 stations of Poland and Hungary based on 30 years periods (1966-1995 and 1961-1990, respectively). Eight weather elements were pre-selected, and reduced to four, by Factor Analysis. They are the mean temperature, relative humidity, cloudiness and wind speed. The redundant elements are diurnal temperature amplitude, water vapour pressure, precipitation and sunshine duration. The omitted elements will be used for independent validation of the classification. Next, hierarchical cluster analysis is performed, having tested various other approaches, leading to six classes in Hungary and southern Poland and to eight classes in the rest of Poland, as the most frequent number of classes in all months and stations. Termination of the clustering, i.e. selection of the number classes is performed in an objective process applying three numerical criteria concerning the within-classes cumulated distance measures. Finally, the types have been re-defined by the method of K-means clustering. The obtained local classifications are compared to the macro-circulation types, based on variance “explaining” capacity concerning the above four basic and four independent variables. In overwhelming majority of the 12 months and 14 stations and 8 variables the obtained local types reduce the variances more effectively than the compared Péczely (1957) types for Hungary and the amalgamated Hess-Brezowsky (1969) types (Mika et al., 1999). These types are important tools in understanding the role of weather in the environmental indicators and in detection of climate change by presenting the processes in terms of weather types. Examples of both applications will be presented in the lecture and in its written version.