Abstract: An automatic generic tool is developed to identify the morphological growth phases of microbiological data types using computer-vision and statistical modelling techniques. In algae phage (phage) typing, representative profiles of morphological growth stages of different algae types are extracted. Present systems rely on the subjective reading of the growth profiles by a human expert which is time consuming and prone to errors. The statistical methodology existing in this work, provides for an automated, objective and robust analysis of the visual image data, along with the facility to cope with increasing data volumes. Validation is performed by comparison to an expert manual segmentation and labelling of the growth phage profiles. The statistical analysis performed on time series data extracted is important for understanding relationships between parameters, provides insight to the growth curve of micro algae and cyanobacteria (correlation) and an essential step to forecast yield of biomass, etc. or predict the duration to achieve a certain yield of a pigment or protein, etc., for commercial applications. There are a number of methods for modelling time series data and being able to predict specific values; specifically, regression analysis and Analysis of Variance (ANOVA) are foremost among them. Computation of the correlation coefficient aids in better understanding the relationships that exist between various parameters that evolve with time and change with different phases of the growth of the organism (and cyanobacteria). This study focuses on statistical techniques for analysis of time series data.