This paper presents two new methods for analysis of a technical state of large-panel residential buildings. The first method is based on elements extracted from the classical methods and on data about repairs and modernization collected from building documentations. The technical state of a building is calculated as a sum of several groups of elements defining the technical state. The deterioration in this method depends on: - time, which has passed since last repair of element or time which has passed since construction, - estimate of the state of element groups which can be determined on basis of yearly controls. This is a new unique method. it is easy to use, does not need expertise. The required data could be extracted easily from building documentations. For better accuracy the data from building inspections should be applied (in Poland inspections are made every year). The second method is based on the extracted data processing by means of the artificial neural networks. The aim is to learn the artificial neural network configurations for a set of data containing values of the technical state and information about building repairs for last years (or other information and building parameters) and next to analyse new buildings by the instructed neural network. The second profit from using artificial neural networks is the reduction of number of parameters. Instead of more then 40 parameters describing building, about 6-12 are usually sufficient for satisfactory accuracy. This method could have lower accuracy but it is less prone to data errors.