diff --git a/analysis/config.py b/analysis/config.py
index 6599178..452ee19 100644
--- a/analysis/config.py
+++ b/analysis/config.py
@@ -7,6 +7,11 @@
 # study end date. should match date in project.yaml
 end_date = "2022-03-31"
 
+# Vertical plot lines for financial year
+# Leave an empty list if no lines needed
+# If a date is out of range of the graph, it will not be visible
+vertical_lines = ["2020-04-01", "2021-04-01"]
+
 # Define a function for fun, I'm not really happy with this but it works
 # Definitely need some help improving this, need to learn how to write robust funs in py
 def calculate_date(date, months):
diff --git a/analysis/rate_calculations.py b/analysis/rate_calculations.py
index b7dc341..38e7d32 100644
--- a/analysis/rate_calculations.py
+++ b/analysis/rate_calculations.py
@@ -3,7 +3,7 @@
 import pandas as pd
 import os
 from cohortextractor import Measure
-from config import demographics, codelist_path, qof_measure_marker
+from config import demographics, codelist_path, qof_measure_marker, vertical_lines
 from ebmdatalab import charts
 
 from study_definition import measures
@@ -89,6 +89,7 @@
             show_legend=True,
         )
 
+        add_date_lines(bp002_decile_chart, vertical_lines)
         bp002_decile_chart.gcf().set_size_inches(15, 8)
         bp002_decile_chart.gca().set_yticklabels(
             ["{:.0f}%".format(x * 100) for x in bp002_decile_chart.gca().get_yticks()]
@@ -126,6 +127,7 @@
             column_to_plot="rate",
             category=None,
             y_label=None,
+            vlines=vertical_lines
         )
 
         df_total.to_csv(os.path.join(OUTPUT_DIR, "rate_table_total.csv"), index=False)
@@ -149,6 +151,7 @@
             column_to_plot="rate",
             category=value.group_by[0],
             y_label=None,
+            vlines=vertical_lines
         )
 
         df.to_csv(
diff --git a/analysis/utilities.py b/analysis/utilities.py
index a4da8ba..4932bc6 100644
--- a/analysis/utilities.py
+++ b/analysis/utilities.py
@@ -2,6 +2,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
+from dateutil import parser
 import os
 from pathlib import Path
 
@@ -263,6 +264,21 @@ def get_percentage_practices(measure_table):
 
     return np.round((num_practices_in_study / num_practices_total) * 100, 2)
 
+def add_date_lines(plt, vlines):
+    # TODO: Check that it is within the range?
+    for date in vlines:
+        try:
+            plt.vlines(
+                x=[pd.to_datetime(date)],
+                ymin=0,
+                ymax=100,
+                colors="orange",
+                ls="--",
+            )
+        except parser._parser.ParserError:
+            # TODO: add logger and print warning on exception
+            # Skip any dates not in the correct format
+            continue
 
 def plot_measures(
     df,
@@ -271,6 +287,7 @@ def plot_measures(
     column_to_plot,
     category=False,
     y_label="Percentage of achievement",
+    vlines=[]
 ):
     """Produce time series plot from measures table.  One line is plotted for each sub
     category within the category column.
@@ -314,6 +331,8 @@ def plot_measures(
         mdates.ConciseDateFormatter(plt.gca().xaxis.get_major_locator())
     )
 
+    add_date_lines(plt, vlines)
+
     if category:
         plt.legend(df[category].unique(), loc="lower right")