Technical maintenance of machines and equipment in processing industry requires elaborate technical diagnostics systems to recognize the current state and forecast their future state. Creating such a system is a complex task due to multiple factors, with aging in aggressive exploitation environment being an important one. Statistical pattern recognition systems are very suitable to solve problems of technical diagnostics as they produce quantitative estimates of the states. We present the use of a hybrid Bayesian pattern recognition classifier that utilizes statistical and fuzzy paradigms and expresses the measurement information with four types of features (discrete, pseudo-discrete, multi-normal and independent continuous). It uses frequentist and subjective information (from training samples and expert opinion respectively) to identify the unknown parameters of the conditional likelihood density functions of each technical state. We discuss possible sources to collect learning information, and different methods to represent it. The classifier uses three different methods for parameter estimation of the conditional likelihood densities using data fusion. The classification is realised as a discriminant non-linear machine, which incorporates fuzzy approaches at different levels. We develop a novel algorithm for fault prediction without dynamic learning with four possible types of answers. A detailed example of technical diagnostics system for classification and prediction of states of turbomachinery for ammonia synthesis is presented. For the journal bearing diagnostics, we introduce modification of the hybrid Bayesian classifier using pseudo-priors to incorporate rule-based knowledge and improve the classification.