The aim of this thesis was to make a design proposal of a pattern recognition system. The system should be capable to detect falls, but also steps of elderly persons with possible walking limitations. However, in order to evaluate the step detection of elderly, it was considered necessary to let the system detect steps of non-elderly persons with no walking disorders as well, allowing for recognition performance comparisons between the two step pattern types. Two wireless sensors were developed, allowing for collection of synchronized signal data from test persons performing fall and step movements. The author, equipped with a sensor at the hip, was the participant in the fall data collection. The step data collection was carried out in two tests where both sensors, positioned at the hip and ankle, were used. The participants in the first test were non-elderly with no walking disorders, and the participants in the second test were elderly persons with varying walking capabilities. The multivariate normal density was chosen as a model for the recognition algorithm. The combinations of features considered appropriate for input to the algorithm were evaluated by the comparison of probability density plots. Running test files from the data collection with known structures through the feature extractor and algorithm generated the plots. The model parameters used in the evaluation were calculated from training samples, extracted with the feature-pair under test from the collected files. One optimal feature-pair was chosen for each of the three pattern types: fall, step (non-elderly) and step (elderly). The system was compiled allowing for different system configurations. The configuration parameters required for each of the three pattern types were: the optimal feature-pair (for the type of pattern to be recognized) and the model parameters (calculated in the feature evaluation for this feature-pair). Threshold configurations for each pattern type were used to get a Boolean output from the algorithm. The resulting system was evaluated by using test files with known structure (as documented by video recordings) from the data collection. According to the evaluation performed, the system detects both fall and step patterns of elderly satisfactory. In the evaluation, the detection of steps was significantly lower for some of the test persons, compared to the group as a whole. One person also generated more detection of non-existing steps. It is possible that the walking patterns of these persons differ from the rest of the group, which could explain the results. Excluding the outliers from the step evaluation when calculating the detection percentage shows that the performance of the system is pleasing. The detection of steps is lower when detecting steps of elderly compared to non-elderly. This is however expected, since it is usual that elderly have limited walking capabilities compared to non-elderly.