Pattern Analysis has currently become a topic of discussion. A lot of research has been done in the field of Complex Pattern Analysis. It involves automatic detection of patterns in data from the same source and making prediction of the new data coming from the same source. Various techniques were developed which took either one or multiple dimensionalities into account to analyze the existing patterns and predicting the future values. Temperature prediction is a complex process and a challenging task for researchers. It includes expertise in multiple disciplines. The prediction of atmospheric parameters is essential for various applications. Some of them include climate monitoring, drought detection, severe weather prediction, agriculture and production, planning in energy industry, aviation industry, communication, pollution dispersal etc. Accurate prediction of weather parameters is a difficult task due to the dynamic nature of atmosphere. Various models have been developed for predicting the temperature which is based on neural network, fuzzy approach etc. In this paper, wavelet neural network approach is used for temperature prediction. This paper aimed in using wavelet neural networks approach using multiple factors to predict the data values. Temperature data of Taipei, Taiwan of the year 1995-96 is used for result verification. The results obtained are compared with that of other techniques and are found to be better.