Principles of surveillance emphasise among others continuous and systematic processes with need to collect data from different sources and regular or ad hoc outputs. Validity and accuracy of data is usually ascertained during collection phase. Quality of data have different aspects that are related to processes generating and delivering the data. Descriptive analysis can show irregularities and deviations from usual pattern and these should trigger first quality questions. To analyze surveillance data consists of checking its quality and performing descriptive epidemiology - to organize data by time, place and person - in order to understand who is ill, where and when, so as to detect abnormal health events such as clustering or change in trends requiring investigation and public health action. Analysis of data in time include detection of unusual occurrences detected in relation to threshold values. Long term trend may reflect success of preventive or repressive measures or future needs in public health action. For these purposes smoothing techniques may clean the data from cyclicities and interfering factors. Spatial distribution of data is related to natural occurence of peoples with similar population characteristics and enviromnetal factors and their correlation with disease occurence can be evaluated. Death, disease, disability and any individual health event may be analysed regularly or on demand in the "response" phase.